Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An in silico Approach

Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An in silico Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An in silico Approach Atikur Rahman, Md. Takim Sarker, Md Ashiqul Islam, Mohammad Uzzal Hossain, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1650735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pseudomonas aeruginosa PAO1, an omnipresent opportunistic bacterium responsible for acute and chronic infection in immunocompromised individuals, is currently on WHO's lists where new antibiotics are urgently required for those. Finding essential genes and essential hypothetical proteins(EHP) can be crucial in identifying novel druggable targets and therapeutics. This study aims to characterize these EHPs, analyze subcellular and physiochemical properties, PPIs network, non-homologous analysis against humans, virulence factor and novel drug target prediction, and finally structural analysis of the identified target employing around 42 robust bioinformatics tools/databases, the output of which was evaluated using ROC analysis. The study discieverd 18 EHPs from 336 essential genes, with domain and functional annotation revealing that 50% of these proteins belong to the enzyme category. The majority are cytoplasmic and cytoplasmic membrane proteins, with half of them being stable proteins which were subjected to PPIs network analysis. The network contains 261 nodes and 269 edges for 9 proteins of interest, with 11 hubs containing at least three nodes each. Finally, pipeline builder predicts 7 proteins with novel drug targets, 5 non-homologous proteins against human proteome, human anti-targets, human gut flora, and 3 virulent proteins. Among these, homology modeling of NP_249450 and NP_251676 were done and the Ramachandran plot analysis revealed that more than 94% of the residues were in the preferred region. By analyzing functional attributes and virulence characteristics, the findings of this study may facilitate the development of innovative antibacterial drug targets and drugs of Pseudomonas aeruginosa PAO1. Pseudomonas aeruginosa Functional Annotation Protein-protein Interactions Non-homology analysis Therapeutics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Pseudomonas aeruginosa often termed as an opportunistic pathogen, is a rod-shaped, motile, gram-negative and non-fermenting bacteria found ubiquitously in soil and water as well as found in colonies on the animate part of plant and animal including humans [ 1 , 2 ]. Isolates collected from diverse environments reported 272 species of the Pseudomonas genus in which P. aeruginosa PA01 is one of the most commonly used laboratory strains as well as employed to generate publicly accessible genomic resources [ 2 , 3 ]. P. aeruginosa PA01 is the first-ever strain of its species having a completely sequenced genome from a chronic lesion isolate dated from the 1950s. The genome is 6.3 Mbp long that includes 5570 ORFs, roughly 89.4% coding regions, and 0.4% stable RNAs. This was the largest bacterial genome available during the year 2000 when sequenced. However, despite the same species, different genomic and phenotypic changes are found across isolates of P. aeruginosa PA01 strains stored in different laboratories worldwide [ 2 , 4 ]. A broad spectrum of host targets including nematodes, insects, plants, and mammals are susceptible to infection by P. aeruginosa species [ 2 ]. It is found harmless in normal gut microflora but causes dangerous infection in critically ill ICU patients [ 5 ]. This trend in pathogenesis makes them an opportunistic pathogen [ 2 ]. It is regarded to be within the top three causative agents for infection caused by opportunistic pathogens annually in the community as well as related to (10–15) % of hospital-acquired infections [ 6 ]. In 2015, a report from the European Antimicrobial Resistance Surveillance Network (EARS-Net) on European regions revealed that around 13.7% strains of P. aeruginosa had acquired resistance to a minimum of three anti-microbial communities whereas about 5.5% of the strains were resistant against five anti-microbial groups. Every year in the USA alone, roughly 440 deaths and 51,000 infection cases are caused by P. aeruginosa of which over 13% results from multi-drug resistant Pseudomonas strains. As a consequence, P. aeruginosa has been announced as one of the greatest threats to public health amongst the 12 bacterial families from the antibiotic-resistance priority pathogens enlisted by WHO in 2017 [ 7 ]. It is also involved with some other nosocomial infections like bloodstream infection, gastrointestinal infection, and urinary tract infection [ 5 ]. This bacterium poses a devastating impact on lung disease patients with cystic fibrosis (CF). Apart from CF, it is equally deadly for individuals having compromised immune systems like AIDS, cancer, burn lesions, and eye injuries. The situation can get even worse despite having robust antibiotic medication since P. aeruginosa possess a wide spectrum of resistance against antibiotics including aminoglycosides, β-lactams, and fluoroquinolones. Therefore, disease stress subsequently results in organ failure and eventually death [ 2 , 5 ]. P. aeruginosa adopts some survival strategy that helps them to resist environmental stressors and dodging host immune responses [ 8 ]. Some of these survival tools include biofilm formation, enzyme promiscuity, horizontal gene transfer, and quorum sensing [ 5 ]. It is one of the well-studied strains for investigating the bacterial biofilm formation process [ 9 ]. Three polysaccharides, alginate, Pel, and Psl, were discovered to be important for bacterial attachment and biofilm formation in P. aeruginosa PA01 [ 8 ]. Over 500 regulatory genes have been recorded from the P. aeruginosa PA01 genome investigation [ 10 ]. There is still a lot to discover for a better understanding of the intracellular signaling pathways and several other regulatory mechanisms involving many proteins that are still uncharacterized. Thus, domain analysis and functional annotation of essential hypothetical proteins (EHPs) can pave the way to identify new potential targets facilitating the drug repositioning development. Since these EHPs are needed for cellular, biological, and metabolic processes, their deletion or mutation can be fatal to the species. These prospective drug targets may be crucial in the development of antimicrobial drugs [ 11 ]. In this study, an in-silico based approach has adopted for the characterization of proteins with unknown functions via different algorithm-based tools and software. Besides, a network-based analysis was directed to find interaction with critically connected hub proteins that may control major molecular activities together. The pipeline builder was employed to analyze non-homologous proteins against humans, human anti-targets, and the proteome of the gut microbiota, as well as predict virulence factors and novel drug targets. Finally, using reliable software, the structural conformation of our protein of interest with potential druggability was predicted and assessed. Thus, our analysis mainly involves the identification of essential hypothetical proteins in P. aeruginosa PA01 and can further lead to the discovery of novel proteins of therapeutic targets. 2. Materials And Methods 2.1 Sequence retrieval and analysis The full proteome of P. aeruginosa PAO1 (strain ATCC 15692) was retrieved from the NCBI genome database. The bacterial complete genome contains 6.3 million base pairs and 5564 proteins [ 4 ]. The essential genes database (DEG) is then subjected to find out the essential hypothetical proteins (EHPs) from this complete proteome list by employing a series of unique keywords [ 12 ]. To begin, we looked for similar hypothetical proteins where we found 2181 proteins among these 5564 proteins. Following that, we searched for the exact matches of hypothetical proteins and exact matches of conserved hypothetical proteins and found 1540 and 625 hypothetical proteins, respectively. According to the DEG database, this bacterial proteome contains 336 essential proteins (EPs). Essential proteins are those that are inevitable and adequate for a living cell to survive under ideal circumstances. Consequently, we discovered 29 essential hypothetical proteins by manual curation whose genomes were entirely conserved among the 336 EPs. The status (reviewed or unreviewed), annotation score (1–5), structural and functional availability, and other factors were used to further validate these 29 EHPs from the NCBI and UniProt databases. Eventually, we excluded 11 proteins, leaving 18 essential hypothetical proteins whose FASTA sequences were used to facilitate further analysis throughout this study. The complete framework of our investigation is presented in Fig. 1 and all the databases/software used in this study are in Table 1 . Table 1 Bioinformatics resources used in the study Serial no Server/ Database Version Using Reason Link References Functional Annotation 1 DEG 15.2 Finding essential HPs http://tubic.tju.edu.cn/deg/ [ 12 ] 2 GO FEAT 1.0 For functional annotation http://computationalbiology.ufpa.br/gofeat/ [ 13 ] 3 CDART Protein Homology search Domain Architecture https://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi [ 14 ] 4 SMART Identification and annotation of protein domains http://smart.embl-heidelberg.de/ [ 15 ] 5 SUPERFAMILY 1.75 For functional annotation https://supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY/ [ 16 ] 6 Pfam 34.0 Determine protein families http://pfam.xfam.org/ [ 17 ] 7 SVMProt Protein functional family prediction http://bidd.group/cgi-bin/svmprot/svmprot.cgi [ 18 ] 8 CATH 4.3 Protein domains into superfamily http://www.cathdb.info/ [ 19 ] 9 InterPro 84.0 Classification of protein families https://www.ebi.ac.uk/interpro/ [ 20 ] 10 HHPred Sequence similarity searching, prediction of sequence features, and sequence classification. https://toolkit.tuebingen.mpg.de/tools/hhpred [ 21 ] 11 PANNZER Functional annotation of uncharacterized proteins http://ekhidna2.biocenter.helsinki.fi/sanspanz/ [ 22 ] 12 PFP Automated protein function gene ontology prediction https://kiharalab.org/web/pfp.php [ 23 ] 13 ESG Protein Function Prediction https://kiharalab.org/web/esg.php [ 24 ] Subcellular Localization 14 Psortb 3.0.2 Subcellular Localization https://www.psort.org/psortb/ [ 25 ] 15 CELLO v.2.5 Subcellular Localization http://cello.life.nctu.edu.tw/ [ 26 ], [ 27 ] 16 TMHMM v. 2.0 prediction of transmembrane helices in proteins http://www.cbs.dtu.dk/services/TMHMM-2.0/ [ 28 ] 17 Phobius prediction of transmembrane helices in proteins https://phobius.sbc.su.se/index.html [ 29 ] 18 HMMTOP 2.0 prediction of transmembrane helices in proteins http://www.enzim.hu/hmmtop/index.php [ 30 ] 19 CCTOP 1.00 prediction of transmembrane helices in proteins http://cctop.enzim.ttk.mta.hu/ [ 31 ] 20 PROTTER 1.0 predicts the presence and location of signal peptide cleavage sites in amino acid sequences and prediction of transmembrane helices in proteins https://wlab.ethz.ch/protter/start/ [ 32 ] 21 SignalP 4.1 4.1 predicts the presence and location of signal peptide cleavage sites in amino acid sequences http://www.cbs.dtu.dk/services/SignalP-4.1/ [ 33 ] 22 PrediSi Prediction of Signal peptides http://www.predisi.de/ [ 34 ] Physicochemical Properties 23 ProtParam computation of various physical and chemical parameters for a given protein https://web.expasy.org/protparam/ [ 36 ] Protein-Protein Interaction 24 NetworkAnalyst v3.0 PPI construction and visualization https://www.networkanalyst.ca/NetworkAnalyst/uploads/ListUploadView.xhtml [ 39 ] Non-Homology Analysis 25 PBIT Pipeline building for non- homology analysis http://www.pbit.bicnirrh.res.in/ [ 41 ] Virulence Factor Analysis 26 VICMpred functional classification of proteins of bacteria into virulence factors https://webs.iiitd.edu.in/raghava/vicmpred/index.html [ 43 ] 27 VirulentPred VirulentPred is a bacterial virulent protein prediction method http://bioinfo.icgeb.res.in/virulent/ [ 44 ] 28 MP3 predict pathogenic proteins in both genomic and metagenomic datasets http://metagenomics.iiserb.ac.in/mp3/tutorial.php [ 45 ] Druggability Analysis 29 DrugBank 5.0 Identification of information on drugs and drug targets https://go.drugbank.com/ [ 47 ] Secondary Structure Analysis 30 SOPMA Secondary structure prediction https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html [ 48 ] 31 PSIPRED 4.0 Secondary structure prediction http://bioinf.cs.ucl.ac.uk/psipred/ [ 49 ] 3D Structure Analysis 32 SWISS-MODEL Protein 3D structure determination https://swissmodel.expasy.org/ [ 50 ] 33 Robetta Protein 3D structure determination https://robetta.bakerlab.org/ 34 Galaxy Refine Refinement of protein structure http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE [ 52 ] 35 PyMOL Software 2.0 Structure visualization https://pymol.org/2/ Validation Check 36 ERRAT 3D structure validation https://saves.mbi.ucla.edu/ [ 53 ] 37 VARIFY 3D 3D structure validation https://saves.mbi.ucla.edu/ [ 54 ], [ 55 ] 38 PROVE 3D structure validation https://saves.mbi.ucla.edu/ [ 56 ] 39 WHATCHECK 3D structure validation https://saves.mbi.ucla.edu/ [ 57 ] 40 PROCHECK 3D structure validation https://saves.mbi.ucla.edu/ [ 58 ] 41 Ramachandran Plot 3D structure validation http://services.mbi.ucla.edu/SAVES/Ramachandran/ [ 58 ] 42 ROC Analysis This web page calculates a receiver operating characteristic (ROC) curve from data http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html 2.2 Segment I: Functional annotation and Properties Characterization 2.2.1 Functional annotation and domain analysis of EHPs : The functional annotation of 18 Pseudomonas aeruginosa EHPs was unveiled by using numerous publicly accessible databases and tools. To gain more knowledge about the molecular functions and biological processes of the EHPs, we consider protein superfamily, family, conserved domain analysis, and Gene Ontology (GO) analysis. Using an online server GO FEAT, for the functional characterization by homology searching through multiple databases such as NCBI, Uniprot, and EMBL, a preliminary assessment was performed to see if any of the HPs were allocated a family and/or protein domain [ 13 ]. After preliminary evaluation, proteins conserved domains and protein functions based on domain architecture were determined by using CDART [ 14 ] from the conserved domain database (CDD) and SMART [ 15 ], respectively. For functional analysis, SUPERFAMILY 1.75 [ 16 ], Pfam 34.0 [ 17 ], SVMProt [ 18 ], CATH 4.3 [ 19 ], InterPro 84.0 [ 20 ], and HHPred [ 21 ] were used to identify the protein superfamily, functional family, domain, and essential sites based on similarity. PANNZER [ 22 ], PFP [ 23 ], and ESG [ 24 ] tools were used for high-throughput functional annotation of EHPs, which provided gene ontology information with z-scores as well as brief explanations of the annotated protein's functionality. These GO terms facilitate understanding a gene's molecular functions, physiological roles, and cellular mechanism, which refers to the location of the gene's product. We used default parameters for all databases. 2.2.2 Subcellular localization and Transmembrane Helices Analysis Sub-cellular localization of a protein can help to infer much information about that protein’s function. In our study, we employed several databases to annotate the subcellular localization of the selected 9 EHPs which includes PSORTb [ 25 ], CELLO [ 26 , 27 ], TMHMM [ 28 ], Phobius [ 29 ], HMMTOP [ 30 ], CCTOP [ 31 ], PROTTER [ 32 ], SignalP 4.1 [ 33 ], and PrediSi [ 34 ]. According to PSORTb and CELLO, the proteins were distinguished by 5 major cellular position: cytoplasmic, inner membrane, periplasmic, outer membrane and extracellular. To predict transmembrane helices, TMHMM, Phobius, HMMTOP, CCTOP and PROTTER were employed. Information of transmembrane helices location is somehow beneficial for the conformation of possible 3D structure [ 35 ]. Besides, it is necessary to find out signal peptides which is the N-terminal part of a protein. Mainly, they are targeted to the endoplasmic reticulum to the secretory pathway and it is considered as the way of protein localization prediction [ 33 ]. Signal peptides were identified by using these SignalP 4.1, PROTTER and PrediSi databases. 2.2.3 Analysis of physicochemical properties The Expasy’s ProtParam server [ 36 ] was utilized for the analysis of physicochemical properties of 18 selected essential hypothetical proteins (EHPs) which include molecular weight, theoretical pI (Isoelectric Point), Formula, the total number of positively and negatively charged residues, instability index, aliphatic index and grand average of hydropathicity (GRAVY). 2.3 Segment II: Protein-Protein Interaction Network of 9 EHPs 2.3.1 Protein-protein interaction network analysis The function of a protein molecule often is modulated by its surrounding protein networks [ 37 ]. For this reason, it is important to discover the protein network to get an insight into the functional association of a particular protein [ 38 ]. In this study, we have used NetworkAnalyst v3.0 for network building [ 39 ]. We have inputted a list of genes containing 9 EHPs with their Uniprot IDs (Q9HXM8, Q9HWT5, Q9HVM2, Q9HVF5, Q9I5H0, Q9HZL8, Q9HYC8, Q9HXV5, and Q9HUH3) since all of these proteins were found stable through the physicochemical analysis. The Generic PPI option under Protein-protein Interactions (PPI) was checked for further processing. P. aeruginosa PA01 interactome database provided with robust computational prediction and experimentally validated data were adopted for network building. Next, the corresponding network was explored for further analysis in Cytoscape. Cytoscape is a standalone software that enables several topological parameter analyses like discovering the shortest possible path, node degree distribution, clustering hub genes of the network [ 40 ]. 2.4 Segment III: Non-Homology Analysis, Virulence Factor Prediction and Druggability Identification 2.4.1 Non-homology analysis against human proteome and human anti-targets Several features were needed for the identification of the drug target for any human diseases. For this reason, to analyze non-homology aspects, we tried Pipeline builder for identification of target (PBIT) server for the non-homology analysis against human proteome, against human anti-targets and human gut flora proteomes [ 41 ]. Using the pipeline builder, we first identified human homologous proteins that share high sequence similarity with human proteome. The sequence similarity of the inputted 9 sequences was figured using BLAST algorithm where E-value > 0.005 and % sequence identity < 50 was set. 8 of the 9 input sequences are non-homologous that were selected for further investigation. These homologous proteins were filtered to avoid the undesirable toxic-effects for these similarities. Filtered and selected 8 non-homologous proteins were further employed in the pipeline to recognize non-homologous proteins against human anti-targets. Proteins that contain harmful effects due to the impact of a drug named anti-targets [ 41 ]. To screen out the significant similar sequence with familiar human anti-targets, PBIT database uses BLAST algorithm where they utilize those human anti-targets proteins based on different literature [ 41 ]. Again E-value > 0.005 and % sequence identity < 50 was set and all non-homologous sequences were selected. 2.4.2 Non-homology analysis against human gut flora proteomes PBIT also analysis human gut flora proteomes that make it easier to find out those highly similar sequences with human gut microbiota. It is known that gut microbiota plays an important role in human health that includes immune, metabolic and neurobehavioral characters [ 42 ]. That’s why it is necessary to design such drugs whose target is non-homologous protein sequence of the gut microbiome. As result, such drugs could not be able to kill or hamper essential microbes found in human gut. For this, Pipeline builder for identification of target (PBIT) server was again used to identify non-homologous proteins against gut microbiota proteomes. As the third step of the pipeline builder, selected proteins were employed where E-value > 0.001and % sequence identity < 50 was set. Now non-homologous proteins were selected for the next investigation. 2.4.3 Analysis of virulence factor Understanding the pathogenesis mechanism through the analysis of virulence factors can be a key to the discovery of new promising therapeutic targets [ 38 ]. Therefore, we have used VICMpred [ 43 ], VirulentPred [ 44 ] and MP3 [ 45 ] for the identification of the virulence property of the 9 EHPs. We have collected the results predicted combinedly by 2 out of the 3 tools. All the results were collected by using the provided default options by the servers. 2.4.4 Druggability analysis and New Target Identification Identification of a new drug target can be a new window for the discovery and development of a new drug against infectious or serious disease [ 46 ]. Druggability analysis is the examination of a protein that has the possible capability or binding affinity towards a drug or drug-like molecules. This Druggability analysis can introduce a new drug target against a drug. Here we used DrugBank, a comprehensive, online database that contains information on drugs and drug targets [ 47 ]. Target identification segment was utilized for this purpose and amino acid sequences in FASTA Format was the searching index. All other BLAST Parameters and Filters were set as default where the Expectation value was set 0.00001. 2.5 Segment IV: Structure Prediction and Structure Validation 2.5.1 Secondary Structure Analysis The interactions between neighboring polypeptides mainly design a protein’s secondary structure. When the elements of the secondary structure have folded together among each other, the 3D structure of the protein is formed. The databases namely SOPMA [ 48 ] and PSIPRED [ 49 ] provide the secondary structure of a protein. These databases were used to predict the structure where protein sequence in FASTA format was the searching index for the websites and the rest of the parameters were set as default. 2.5.2 Essential hypothetical proteins 3D structure modelling The protein 3D structure was determined based on two methods: template-based homology modelling, and trRosetta methods. The three-dimensional structure of the targeted protein was generated using the SWISS-MODEL server, which uses template search and then aligns the target sequence with the template structure to create the homology model [ 50 ]. To construct the model with an accuracy equal to low-resolution x-ray crystallography, we only consider templates with ≥ 30% sequence identity. Then the server, Robetta ( https://robetta.bakerlab.org/ ) was employed to predict the 3D model by using trRosetta algorithm. It is a deep learning method based on direct energy minimizations that is the most accurate process of structure building provided by this server [ 51 ]. Finally, the built structure was optimized using Galaxy Refiner, with the best-refined model based on the lowest MolProbity and highest GDT-HA value [ 52 ]. Consequently, PyMOL 2.0 visualization software is used to visualize all of the refined structure files, which are in .pdb format. 2.5.3 Protein structure validation assessment: The reliability of a predicted 3D structure of a protein can be assessed by using various quality assessment tools. Here, in this study, we used SAVES version 6.0 ( https://saves.mbi.ucla.edu/ ) which is a meta-server that runs six programs at once to check and validate protein structure during and after model refinement. This server validates the stereochemical consistency of a protein structure by performing residue by residue geometry and overall structure geometry. Furthermore, it also compares the results to good structures to see if an atomic model (3D) is compatible with its own amino acid sequence (1D) by assigning a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar, etc.). We run ERRAT [ 53 ], VARIFY 3D [ 54 , 55 ], PROVE [ 56 ], WHATCHECK [ 57 ], PROCHECK [ 58 ], and Ramachandran Plot [ 58 ] from SAVES v6.0 to determine the consistency of the construct model. 2.6 Performance assessment of the Study In our study, we have applied the receiver operating characteristic (ROC) analysis for validating the accuracy of our bioinformatics tools used for the functional annotation of EHPs from P. aeruginosa [ 59 ]. We have collected 100 arbitrary protein functions of P. aeruginosa along with their gene names using the same pipeline used prior to our study in the Supplementary excel file . Two integer values namely “1” as a truly positive and “0” as a truly negative were assigned to classify the prediction. The confidence rating was denoted by “2”, “3”, “4” and “5” respectively. The higher number denotes greater level of confidence. The input file consists of 2 columns where 1st column contains binary numbers like 1 (True positive) and 0 (True negative) and the 2nd column contains a rate of confidence ranging from 2 to 5. For the present study, six levels were considered for determining the diagnostic efficacy. The ROC analysis was used for 12 individual functional annotation tools. The data were submitted to an online-based ROC curve generating web server in format-1 [ 60 ]. The output result includes accuracy, sensitivity, specificity and the ROC area ( Supplementary File 1 ). The accuracy of our adopted pipeline is 97.42% which indicates a very high and reliable result for the bioinformatics tools that we used in our study. 3. Results 3.1 Functional annotation and domain analysis of EHPs The functional annotation of the 18 EHPs was examined using 12 reliable platforms that predict protein superfamily, family, conserved domains, and Gene Ontology terms (GO). Here, the functional annotation was assigned with high confidence as we considered only that function that was similar in three or more programs. Consequently, the functional characterization categorizes these proteins into 9 functional categories, namely enzymes (deaminases, dehydrogenases, helicases, transferases, DNases, oxidoreductases, kinases, etc), transporter protein, bacterial outer membrane protein, folate binding protein, peptidase inhibitor protein, electron transporter protein, chromosome partition protein, ribosome maturation protein, pathogenesis-related protein. Nine of the 18 EHPs are enzymes (NP_252456.1, NP_252782.1, NP_253095.1, NP_253326.1, NP_250846.1, NP_252375.1, NP_253678.1, NP_253679.1, NP_253685.1), two are transporter proteins (NP_253252.1, NP_251676.1), and the remaining seven proteins are in the seven groups. Table 2 enlists the 18 EHPs superfamily, functional family, molecular functions, biological functions, as well as their GO IDs and database IDs. Among these proteins, NP_249450.1 is a member of the folate-binding superfamily, with the aminomethyl transferase folate-binding domain as its functional family. Aminomethyl transferase and transaminase activity are the two molecular functions of this protein. Another protein sequence of NP_251676.1 was predicted belonging to the functional family that represents the periplasmic core domain found in a variety of ABC transporters. ATP binding, ATPase-coupled xenobiotic transmembrane transporter activity, efflux transmembrane transporter activity, and ATPase activity are some of the molecular functions of this protein. According to the GO annotation, there were 65 GO terminologies in total for the molecular function and biological process. These GO IDs can be used to retrieve Gene Ontology analysis of these 18 EHPs. Table 2 Functional annotations of 18 essential hypothetical proteins Serial No. RefSeq Superfamily Family Gene ontology Go ID/Database integration Biological process Molecular function 1 NP_252456.1 Cytidine deaminase- like Deoxycytidylate deaminase-like 1. tRNA wobble adenosine to inosine editing 1.Hydrolase activity 2.Zinc ion binding 3.Catalytic activity 4.tRNA-specific adenosine 34-deaminase activity (GO:0002100) (GO:0016787) (GO:0008270) (GO:0003824) (GO:0052717) Uniprot (W1MGT3) Interpro (W1MGT3) Interpro (IPR016192) Interpro (IPR002125) Interpro (IPR016193) Interpro (IPR028883) Pfam (PF14437) NCBI (532131853) EMBL (ATNK01000135) 2 NP_252782.1 Hotdog Thioesterase / thiol ester dehydratase-isomerase Thioesterase 1. Histidine biosynthetic process 1. Histidinol dehydrogenase activity 2. Zinc ion binding 3. NAD binding (GO:0000105) (GO:0004399) (GO:0008270) (GO:0051287) Uniprot (A0A448BY09) Interpro(A0A448BY09) Interpro(IPR029069) Interpro (IPR006683) Pfam (PF03061) EMBL (LR134300) 3 NP_253095.1 Uncharacterized protein Dna[CI] antecedent, DciA 1. Protein dephosphorylation 1. Zinc ion binding 2. Protein tyrosine/serine/threonine phosphatase activity (GO:0006470) (GO:0008270) (GO:0008138) Uniprot(Q9HW03) Interpro (Q9HW03) KEGG(pae:PA4405) KEGG GM(pae:PA4405) Interpro (IPR007922) Pfam (PF05258) NCBI(489212117) EMBL (AE004091) 4 NP_253252.1 MATE_like Lipid II flippaseMurJ, Polysaccharide biosynthesis C-terminal domain 1. Cell wall organization 2. Peptidoglycan biosynthetic process 3. Regulation of cell shape 1. Lipid-linked peptidoglycan transporter activity (GO:0071555) (GO:0009252) (GO:0008360) (GO:0015648) Uniprot (W1MQM4) Interpro (W1MQM4) Interpro (IPR004268) Pfam (PF03023) NCBI (532135099) EMBL (ATNK01000069) 5 NP_253326.1 Glycerol-3-phosphate (1)-acyltransferase Glycerol-3-phosphate (1)-acyltransferase 1. D-galacturonate catabolic process 2. D-glucuronate catabolic process 1. Transferase activity, transferring acyl groups Uniprot (Q9HVF5) Interpro (Q9HVF5) KEGG (pae: PA4636) KEGG GM(pae:PA4636) Interpro (IPR002123) Pfam (PF01553) NCBI (489205664) EMBL (AE004091) 6 NP_253368.1 TonB-dependent receptor family Energy transducer TonB 1. Viral process 1. GTP binding (GO:0016032) (GO:0005525) Uniprot (Q9HVB6) Interpro (Q9HVB6) KEGG (pae:PA4679) KEGG GM(pae:PA4679) NCBI (489212281) EMBL (AE004091) 7 NP_249450.1 Folate-binding Aminomethyl transferase folate-binding domain 1. Iron-sulfur cluster assembly 2. Glycine decarboxylation via glycine cleavage system 1. Aminomethyl transferase activity 2. Transaminase activity (GO:0019464) (GO:0004047) (GO:0008483) Superfamily (GO:0016226) Uniprot (Q9I5H0) Interpro (Q9I5H0) KEGG (pae:PA0759) KEGG GM(pae:PA0759) Interpro (IPR029043) Interpro (IPR017703) NCBI (489205124) EMBL (AE004091) 8 NP_250659.1 Inhibitor_I78 Peptidase inhibitor I78 family 1. Cell adhesion 2. Homophilic cell adhesion via plasma membrane adhesion molecules 1. Calcium ion binding 2. Serine-type endopeptidase inhibitor activity (GO:0007155) (GO:0007156) (GO:0005509) (GO:0004867) SMART Uniprot (Q9I2D5) Interpro (Q9I2D5) KEGG (pae:PA1969) KEGG GM (pae:PA1969) Interpro (IPR021719) Pfam (PF11720) NCBI (489210309) EMBL (AE004091) 9 NP_250846.1 DNase I-like Endonuclease/Exonuclease/phosphatase N/A 1. Endonuclease activity 2. Exonuclease activity SMART (GO:0004519) (GO:0004527) Uniprot (A0A6N0KLP9) Interpro (A0A6N0KLP9) Interpro (IPR036691) Interpro (IPR005135) Pfam (PF03372) EMBL (CP054572) 10 NP_251676.1 LolE MacB-like periplasmic core domain, Lipoprotein-releasing ABC transporter permease 1. Lipoprotein localization to outer membrane 2. Lipoprotein transport 3. Protein localization to outer membrane 1. ATP binding 2. ATPase-coupled xenobiotic transmembrane transporter activity 3. Efflux transmembrane transporter activity 4. ATPase activity SMART (GO:0044874) (GO:0042953) (GO:0089705) (GO:0005524) (GO:0008559) (GO:0015562) (GO:0016887) Uniprot (Q9HZL8) Interpro (Q9HZL8) KEGG (pae:PA2986) KEGG GM (pae:PA2986) Interpro (IPR003838) Interpro (IPR011925) Interpro (IPR025857) Pfam (PF02687) Pfam (PF12704) NCBI (489210993) EMBL (AE004091) 11 NP_252171.1 Fe-S cluster assembly (FSCA) domain-like Iron-sulfur cluster assembly protein 1. Iron-sulfur cluster assembly 1.ATPase activity 2.ATP binding 3.Iron-sulfur cluster binding 4.Metal ion binding SMART (GO:0016226) (GO:0016887) (GO:0005524) (GO:0051536) (GO:0046872) Uniprot (A0A3S4MTX6) Interpro(A0A3S4MTX6) Interpro (IPR034904) Interpro (IPR002744) Interpro (IPR019591) Interpro (IPR000808) Interpro (IPR027417) Interpro (IPR033756) Pfam (PF01883) Pfam (PF10609) EMBL (LR134300) 12 NP_252375.1 Carbam_trans_N (Carbamoyltransferase N-terminus) tRNA N6-adenosine threonyl carbamoyltransferase 1. tRNA threonyl carbamoyl adenosine modification 1. Metalloendopeptidase activity 2. Iron ion binding 3. N(6)-L-threonyl carbamoyl adenine synthase activity SMART (GO:0002949) (GO:0004222) (GO:0005506) (GO:0061711) Uniprot (Q9HXV5) Interpro (Q9HXV5) KEGG (pae:PA3685) KEGG GM (pae:PA3685) Interpro (IPR043129) Interpro (IPR000905) Interpro (IPR022496) Pfam (PF00814) NCBI (887492937) EMBL (AE004091) 13 NP_253374.1 MukE (MukE is part of the MukBEF condensin complex) Bacterial condensin subunit MukE 1. Cell cycle 2. Cell division 3. DNA replication 4. Chromosome segregation 5. Chromosome condensation 1. GTP binding 2. GTPase activity 3. Translation elongation factor activity 4. ATP binding (GO:0007049) (GO:0051301) (GO:0006260) (GO:0007059) (GO:0030261) (GO:0005525) (GO:0003924) (GO:0003746) (GO:0005524) Uniprot (A0A448BSU5) Interpro (A0A448BSU5) Interpro (IPR042038) EMBL (LR134300) 14 NP_253434.1 1.RimP N-terminal domain 2.RimP C-terminal SH3 domain (also known as yhbC) RimP N-terminal domain, RimP C-terminal SH3 domain 1.Ribosomal small subunit biogenesis N/A SMART (GO:0042274) Uniprot (A0A3S4MTG9) Interpro (A0A3S4MTG9) Interpro (IPR003728) Interpro (IPR028998) Interpro (IPR036847) Interpro (IPR028989) Interpro (IPR035956) Pfam (PF02576) Pfam (PF17384) EMBL (LR134300) 15 NP_253455.1 Bet v1-like Polyketide cyclase /dehydrase and lipid transport 1. Ubiquinone biosynthetic process 2. Cellular respiration 1. Ubiquinone binding (GO:0006744) (GO:0045333) (GO:0048039) SUPERFAMILY 1.75 SMART IPR005031 Uniprot (A0A448BT54) Interpro (A0A448BT54) Interpro (IPR005031) Interpro (IPR023393) Pfam (PF03364) EMBL (LR134300) 16 NP_253678.1 FAD/NAD(P)-binding domain FAD dependent oxidoreductase 1.Oxidation-reduction process 1.Oxidoreductase activity SMART (GO:0055114) (GO:0016491) 17 NP_253679.1 NAD(P)-linked oxidoreductase/ Aldo-keto reductase (AKR) superfamily Aldo/keto reductase family 1. Daunorubicin metabolic process 2. Doxorubicin metabolic process 1.Oxidoreductase activity 2.D-threo-aldose 1-dehydrogenase activity SMART (GO:0044597) (GO:0044598) (GO:0047834) Uniprot (A0A3S4Q0Y1) Interpro (A0A3S4Q0Y1) Interpro(IPR023210) Interpro (IPR036812) Pfam (PF00248) EMBL (LR134300) 18 NP_253685.1 Protein kinase-like (PK-like) Phosphotransferase enzyme family 1. Protein phosphorylation 1. ATP binding 2. Protein serine/threonine kinase activity Pfam (GO:0006468) (GO:0005524) (GO:0004674) Uniprot (A0A3S5E573) Interpro (A0A3S5E573) Interpro (IPR011009) EMBL (LR134300) 3.2 Subcellular Localizations of EHPs To identify the cellular localization of our 18 EHPs, the websites PSORTb and CELLO were utilized. According to the data of PSORTb, among 18 essential hypothetical proteins, 6 proteins belong to cytoplasmic protein, 8 proteins belong to the location of the cytoplasmic membrane and the remaining 4 proteins are considered as unknown. The database, CELLO depicted that 14 proteins are cytoplasmic protein, 2 proteins are considered as inner membrane protein and the rest 2 are periplasmic protein. This is the generalized concept of the cellular location which is shown in Fig. 2 and supplementary table 1 . The existence of the transmembrane helix was also figured out and this can help to carry out the function of a protein through transmembrane transportation. The amount of transmembrane helix was given in supplementary table 1 . The presence of signal peptide was also investigated from the three websites SignalP 4.1, PROTTER and PrediSi. Among 18 proteins 14 proteins (NP_252456.1, NP_252782.1, NP_253095.1, NP_253326.1, NP_253368.1, NP_249450.1, NP_250846.1, NP_252171.1, NP_252375.1, NP_253374.1, NP_253455.1, NP_253678.1, NP_253679.1 and NP_253685.1) do not contain any signal peptide and one protein contain signal peptide unanimously. Whereas the remaining proteins (NP_253252.1, NP_251676.1 and NP_253434.1) are containing signal peptides from any of a website ( supplementary table 1 ). 3.3 Physicochemical properties Analysis We have searched for the physicochemical properties of 18 EHPs which is shown in Table 3 . All the proteins had molecular weight ranging from 13335.11 to 56122.54. The highest molecular weight was observed to be 56122.54 for the NP_253252.1 protein, a probable lipid II flippaseMurJ [ 61 ]. The theoretical pI (Isoelectric Point) indicates the pH at which the charge of an amino acid of a protein remains neutral. Therefore, no movement occurs when placed in an electric field with a direct current. This parameter comes in handy as proteins are dense and stable at an isoelectric pH [ 62 ]. The theoretical pI ranged from 4.52 to 10.71. Both of these parameters (molecular weight and theoretical pI) help visualize the Two-dimensional gel electrophoresis or (2-DE). Hence contributes to the scientific examinations of these hypothetical proteins [ 63 ]. The aliphatic index can be an effective indicator for determining the thermostability of some protein molecules [ 64 ]. A protein molecule with a higher aliphatic index indicates its higher range of temperature at which it gains its thermostability [ 65 ]. The aliphatic index tabulated for our protein group ranged from 83.13 to 133.96. The NP_253252.1 protein showed the maximum thermostability and NP_252456.1 with the lowest. The parameter called Instability index determines a protein whether it’s stable or unstable in a test tube [ 66 ]. For our analysis, we set the cutoff value to 40 where the value below 40 indicates a protein to be stable and above 40 predicts it as an unstable protein. Total 9 proteins (NP_252456.1, NP_252782.1, NP_253252.1, NP_253326.1, NP_249450.1, NP_251676.1, NP_252171.1, NP_252375.1, NP_253679.1) out of 18 proteins of interest found to be stable with Instability index values of 25.65, 37.46, 36.08, 38.61, 31.65, 38.17, 32.05, 29.03, 32.97 respectively. The grand average of hydropathy (GRAVY) determines the extent of protein-water interaction which is calculated by dividing the aggregate of all the amino acids hydropathy values with the total number of residues in the given sequence [ 65 , 67 ]. the GRAVY values lied between − 0.427 to 0.857. The lower the GRAVY value, the more a protein interacts with water [ 65 ]. The NP_253095.1 protein was found to be most interactive among all these proteins having a GRAVY value of -0.427. Table 3 Physicochemical properties of 18 essential hypothetical proteins Serial No. RefSeq. Molecular weight Theoretical pI Formula Total number of negatively charged residues (Asp + Glu) Total number of positively charged residues (Arg + Lys) Instability index (II) Aliphatic index Grand average of hydropathicity (GRAVY) 1 NP_252456.1 19937.91 9.12 C 869 H 1409 N 265 O 255 S 9 22 26 25.65 (Stable) 83.13 -0.257 2 NP_252782.1 14871.24 7.93 C 658 H 1078 N 188 O 193 S 5 15 16 37.46 (Stable) 100.29 0.162 3 NP_253095.1 15057.34 10.71 C 657 H 1080 N 210 O 188 S 4 12 21 57.65 (Unstable) 93.28 -0.427 4 NP_253252.1 56122.54 10.03 C 2643 H 4201 N 651 O 651 S 19 21 40 36.08 (Stable) 133.96 0.857 5 NP_253326.1 43779.82 6.85 C 1955 H 3058 N 554 O 569 S 11 51 50 38.61 (Stable) 87.02 -0.376 6 NP_253368.1 24873.64 5.30 C 1111 H 1779 N 317 O 319 S 6 28 25 73.37 (Unstable) 91.89 -0.119 7 NP_249450.1 33667.59 5.37 C 1492 H 2415 N 425 O 446 S 7 39 32 31.65 (Stable) 108.22 0.057 8 NP_250659.1 13335.11 8.98 C 567 H 936 N 178 O 181 S 6 12 15 53.34 (Unstable) 83.54 -0.085 9 NP_250846.1 27693.92 9.90 C 1245 H 1962 N 380 O 330 S 5 23 30 52.29 (Unstable) 100.69 -0.229 10 NP_251676.1 47387.94 9.69 C 2139 H 3484 N 582 O 587 S 20 34 44 38.17 (Stable) 114.85 0.365 11 NP_252171.1 38888.77 5.26 C 1711 H 2780 N 482 O 517 S 16 40 31 32.05 (Stable) 102.34 0.090 12 NP_252375.1 24180.71 5.02 C 1081 H 1707 N 303 O 313 S 7 27 19 29.03 (Stable) 102.48 0.166 13 NP_253374.1 26354.58 4.52 C 1166 H 1811 N 315 O 366 S 8 43 19 53.53 (Unstable) 89.96 -0.366 14 NP_253434.1 17171.46 4.59 C 763 H 1208 N 206 O 236 S 4 27 15 57.54 (Unstable) 105.07 -0.197 15 NP_253455.1 16000.46 6.72 C 720 H 1130 N 190 O 208 S 7 14 14 43.00 (Unstable) 88.12 -0.037 16 NP_253678.1 42109.34 7.73 C 1866 H 3011 N 551 O 541 S 9 47 48 48.61 (Unstable) 98.72 -0.130 17 NP_253679.1 29030.07 6.00 C 1281 H 2067 N 373 O 386 S 5 36 31 32.97 (Stable) 101.22 -0.101 18 NP_253685.1 24985.76 9.60 C 1112 H 1791 N 337 O 311 S 4 27 34 45.71 (Unstable) 104.77 -0.365 3.4 Protein-protein interaction network analysis The PPI represents the connection among the 9 stable EHPs and their corresponding functionally relative proteins from P. aeruginosa PA01. The network has 261 nodes and 269 edges for 9 proteins of interest. Here, the network is provided with 11 subnetworks (Hubs) with a minimum of 3 nodes each. The nodes with only 3 connections (Degree) are considered as Islands (ostA and PA1847) (Table 4 ) [ 68 ]. The node degree and Betweenness centrality range from 3 to 45 and 4750 to 17881.76, respectively. The interaction among the hub proteins can be seen in Fig. 3 . The size and color gradient of the nodes determine the degree of a protein. A node degree reveals the extent of interaction of a particular node with other nodes. The nodes with lower degree values are colored green namely PA4992 (24), PA3481 (23), PA4093 (20), PA4636 (18). The color gradually turned into deep purple by the increase of node degree values. Nodes with enlarged size similarly denotes increased node degree values such as PA2986 (45), PA0759 (41), PA4562 (38), PA3685 (32), PA3767 (28) (Fig. 3 ) (Table 4 ). The nodes in cyan blue meaning 2 or more interactions with their corresponding subnetworks. Betweenness centrality is a topological measure that typically determines the number of shortest paths through nodes. The nodes with a higher degree and betweenness centrality values represent vital proteins for signal trafficking of the cellular system [ 68 ]. The function of all proteins in the network are collected from NCBI using their associated Entrez IDs and listed in the supplementary table 2 . Table 4 List of proteins with their Reference sequence, Uniprot ID, Protein name, Node degree value, and Betweenness centrality. Serial No. Ref seq. UniProt ID Protein name Degree Betweenness centrality 1 NP_251676.1 Q9HZL8 PA2986 45 9681.83 2 NP_249450.1 Q9I5H0 PA0759 41 17881.76 3 NP_253252.1 Q9HVM2 PA4562 38 9315.74 4 NP_252375.1 Q9HXV5 PA3685 32 8742.58 5 NP_252456.1 Q9HXM8 PA3767 28 11489.25 6 NP_253679.1 Q9HUH3 PA4992 24 10103.17 7 NP_252171.1 Q9HYC8 PA3481 23 5329.08 8 NP_252782.1 Q9HWT5 PA4093 20 4750.0 9 NP_253326.1 Q9HVF5 PA4636 18 6466.58 10 NP_249286.1 Q9I5U2 ostA 3 10848.52 11 NP_250538.1 Q9I2P8 PA1847 3 5698.26 3.5 Non-homology analysis against human proteome, human anti-targets and human gut flora proteomes To introduce a novel target for a drug it must be non-homologous against human proteome, human anti-targets, human gut flora proteomes. Utilizing pipeline builder from the Pipeline builder for identification of target (PBIT) server, 9 protein sequences were inputted to find out the highly similar sequence with human proteome. Among the 9 EHPs sequences, one sequence was homologous with the human proteome. Filtering that one sequence, 8 non-homologous proteins were selected for the next pipeline analysis to find out the non-homologous proteins against human anti-targets. Among that 8 entered sequences, significant similar sequences of human anti-target proteins were screen out. This result depicted that 7 proteins are non-homologous and one protein is homologous to the human anti-target where this one homologous protein was omitted from the study. After the filtration, selected 7 proteins were further inputted onto the pipeline builder to analyze non-homologous proteins against human gut flora proteomes. This time 2 proteins were screen out because of containing high sequence similarity with the proteomes of the beneficiary microbes belong to the human gut. Then finally the sequences of 5 non-homologous EHPs were selected for the next parameter of finding virulence capability. The details of the non-homology analysis are given in Table 5 . Table 5 Aspects of the proteins like non-homology to human proteins and proteins of human gut flora, virulence of the pathogen, druggability for the 9 EHPs Serial no Protein Non-homology analysis against human proteome Non-homology analysis against human anti-targets Non-homology analysis against gut microbiota proteomes Virulence analysis Druggability analysis 1 NP_252456.1 Non-homologous Non-homologous Non-homologous Non- virulent Old target 2 NP_252782.1 Non-homologous Non-homologous Non-homologous Non- virulent Novel target 3 NP_253252.1 Non-homologous Non-homologous Homologous Non- virulent Novel target 4 NP_253326.1 Non-homologous Non-homologous Non-homologous Non- virulent Novel target 5 NP_249450.1 Non-homologous Non-homologous Non-homologous Virulent Novel target 6 NP_251676.1 Non-homologous Non-homologous Non-homologous Virulent Novel target 7 NP_252171.1 Homologous Non-homologous Non-homologous Non- virulent Novel target 8 NP_252375.1 Non-homologous Non-homologous Homologous Non- virulent Novel target 9 NP_253679.1 Non-homologous Non-homologous Non-homologous Non- virulent Old target 3.6 Virulence factor The virulent EHPs from P. aeruginosa PA01 are enlisted in Table 5 . VICMpred is a Support Vector Machine (SVM) based webserver that predicted all of the 9 EHPs as non-virulent with 70.75% accuracy [ 43 ]. VirulentPred is also based on bi-layer cascade SVM with five-fold increased cross-validation methods that give 81.8% prediction accuracy [ 44 ]. Total 3 proteins namely NP_249450.1 (e-106), NP_251676.1 (e-171), NP_253679.1 (7e-77) were predicted as virulent by VirulentPred in p. aeruginosa PA01 strain utilizing the Similarity-Based search through PSI-BLAST. Another webserver called MP3 uses an integrated SVM-HMM approach which commonly predicted NP_251676.1 as a pathogenic protein. 3.7 A Possible New Drug Target Identification Along with the two virulent EHPs, other 7 sequences of EHPs were employed to the DrugBank server for the identification of potentially new drug candidates. This server showed that NP_252456.1 contains one drug target against the drug Imidazole (E value: 5.62144e-18; Bit score: 75.485; Query length: 182; Alignment length: 77) and two drug targets were exhibited by the protein NP_253679.1 against the drug Nicotinamide adenine dinucleotide phosphate (E value: 3.79487e-15; Bit score: 72.4034; Query length: 270; Alignment length: 213) and Nicotinamide adenine dinucleotide phosphate (E value: 1.77261e-14; Bit score: 70.8626; Query length: 270; Alignment length: 217). The remaining 7 proteins (NP_252782.1, NP_253252.1, NP_253326.1, NP_249450.1, NP_251676.1, NP_252171.1 and NP_252375.1) was considered as a fresh or new drug target by the DrugBank database. This website also revealed that our targeted two proteins named NP_249450.1 and NP_251676.1 displayed zero matches for the drug target which means they are new potential drug candidates with druggability. The overall results are in Table 5 . 3.8 Analyzing Secondary Structure Based on the findings of segment III, we selected two proteins for the next level investigations that match all the criteria of segment III. As they are hypothetical proteins, they must lack some information. For this, to suggest them as a new drug target we explored their secondary structure. SOPMA and PSIPRED were the web tools that were used for the secondary structure analysis. According to the SOPMA server, the secondary structure of NP_249450.1 had Alpha helix (Hh): 126 (40.13%); Extended strand (Ee): 57 (18.15%); Beta turn (Tt): 20 (6.37%), and Random coil (Cc): 111 (35.35%) where the parameters were set as Window width: 17; Similarity threshold: 8 and Number of states: 4. The protein, NP_251676.1 had Alpha helix (Hh): 206 (47.58%); Extended strand (Ee): 83(19.17%); Beta turn (Tt): 23(5.31%) and Random coil (Cc): 121 (27.94%) with the same parameter as before. The results from SOPMA database for both proteins are given in supplementary table 3 . The PSIPRED sequence plot and PSIPRED cartoon plot were provided as a result of the PSIPRED web servers. The sequence plot and cartoon plot structure described that goldenrod (semi-yellow) color is for the extracellular strand domain, pink color is for helix; grey color is for coil and blackish blue is for the confidence of the structure. According to this, NP_249450.1 showed more coil in its secondary structure whereas NP_251676.1 showed more helix. Figure 4 (a) is the secondary structure of NP_249450.1 from PSIPRED and Fig. 4 (b) is from SOPMA websites. Besides Fig. 5 (a) is the secondary structure of NP_251676.1 from PSIPRED and Fig. 5 (b) is from SOPMA database. 3.9 Essential hypothetical proteins 3D structure modelling Only proteins that passed all of the above-mentioned pipeline analyses were assigned a three-dimensional structural conformation. Two proteins, NP_249450.1 and NP_251676.1, were subjected to a thorough pipeline review and thus have the potential to be used as new drug targets. As a result, these two proteins were subjected to 3D structural conformation determination using two methods: template-based homology modelling from SWISS-MODEL and ab-initio modeling using the trRosetta algorithm from the Robetta server. For template-based homology modelling, we searched for templates from SWISS-MODEL for these two proteins. 1vly.1 and 6f3z.2 were the best template for NP_249450.1 and NP_251676.1, respectively. The templates were chosen based on several parameters, including the Global Model Quality Estimation (GMQE), Qualitative Model Energy ANalysis (QMEAN), Z-score, sequence identity, sequence similarity, sequence coverage, oligo-state of the chosen templates, and so on. The template 1vly.1 was actually a 1.30 Å resolution x-ray diffraction crystallography structure of a putative aminomethyltransferase (ygfz) from E. coli . This template shared 30.23% sequence identity with the 314 aa long NP_249450.1 protein, which spans from (4-307) aa. The template 6f3z.2 , on the other hand, was a complex of E. coli LolA and the periplasmic domain of LolC that was also identified by x-ray diffraction crystallography at a resolution of 2.00 Å. The sequence identity was 30.73%, spanning (67–290) amino acids out of the 433 amino acids in the NP_251676.1 protein. Both of these templates had a monomer oligo-state. Finally, using 1vly.1 and 6f3z.2 templates, the structures of NP_249450.1 and NP_251676.1 EHPs were formed, as shown in Fig. 6 a and 6 c. Structure prediction by Robetta server illustrated that the provided model was build using trRefineRosetta modelling ( ab-initio modeling using the trRosettaalgorithm). Structure of NP_249450.1 showed 0.79 score as confidence (Fig. 6 b) while NP_251676.1 showed 0.81 score as confidence (Fig. 6 d). Consequently, the structures from SWISS-MODEL were then refined from Galaxy Refiner where model 2 for NP_249450.1 and Model 5 for NP_251676.1 were downloaded after final refinement. For the NP_249450.1 and NP_251676.1 proteins, the lowest MolProbity was 1.738 (Model 2) and 1.729 (Model 5), respectively, while the initial score was 2.280 and 2.299. Also, the structures from Robbetta were refined from Galaxy Refiner. 3.10 Protein structure validation assessment The predicted protein structure was validated by SAVES v6.0 server which runs six programs simultaneously to evaluate the quality of the build model. The ERRAT value served as the model's overall quality element. The overall quality factor for the NP_249450.1 protein structure from SWISS-MODEL and Robetta, respectively, was 87.6325% and 92.459%. It was 93.3649% and 98.063% for NP_251676.1 from these two servers, respectively. In supplementary Fig. 1 , bar plots depict the overall quality factor from ERRAT. VARIFY3D conducts an analysis in which a structure passes if at least 80% of the amino acids in the 3D/1D profile have a score of > = 0.2. Three of the four structures passed this parameter (two from SWISS-MODEL and one from Robetta), while one structure failed for NP_251676.1 from Robetta. WHATCHECK included a color box with a number within it that reflects 46 different criteria, with the green, yellow, and maroon colors representing OK, warning, and error, respectively. The overall summary report is OK for all four structures. Table 6 included a comprehensive report on the consistency of the four structures that we retrieved from the SAVES v6.0 server. On the contrary, structures from SWISS-MODEL failed to pass the PROVE parameters, while structures from Robetta were placed in warning categories due to atomicB-factors, and the protein atoms having absolute Z-scores > 3. Ramachandran plot analysis from the PROCHECK program also demonstrated that more than 94% of residues were in the most favored region for all four structures from both SWISS-MODEL and Robetta. It was 97.3% for NP_251676.1 protein from Robetta, with 0.0% residues in the disallowed region. Ramachandran plot analysis unveiled that the generated structures of the proteins represent an excellent degree of validity and reliability, which is depicted in Fig. 7 . Table 6: Three-dimensional structure validation of the predicted two hypothetical proteins from SAVES v6.0 server Saves Result Protein Name ERRAT VARIFY 3D PROVE WHATCHECK PROCHECK Ramachandran Plot (% residue in the most favored region) NP_249450.1 (Swiss Model) Overall Quality Factor 87.6325 97.70% of the residues have averaged 3D-1D score >= 0.2 Pass Buried outlier protein atoms total from 1 Model: 6.1% fail 12 34567 891011 12 131415161718 19 20 212223 24 25 2627 28 29 303132 33 3435 36 37 38 39 40 414243 4445 46 Out of 8 evaluations Errors: 3 Warning: 2 Pass: 3 94.6% NP_249450.1 (Robetta) Overall Quality Factor 92.459 94.90% of the residues have averaged 3D-1D score >= 0.2 Pass Buried outlier protein atoms total from 1 Model: 4.1% warning 12 345678 91011 12 131415161718 19 20 21222324 25 2627 28 29 303132 33 34 35363738 39 40 41 42 43 4445 46 Out of 8 evaluations Errors: 3 Warning: 2 Pass: 3 94.0% NP_251676.1 (Swiss Model) Overall Quality Factor 93.3649 82.59% of the residues have averaged 3D-1D score >= 0.2 Pass Buried outlier protein atoms total from 1 Model: 5.4% fail 12 34567 8 9 1011 12 131415161718 19 20 2122232425 26 27 28 29 303132 33 34 35363738 39 40 41 42 43 4445 46 Out of 8 evaluations Errors: 2 Warning: 4 Pass: 2 95.3 % NP_251676.1 (Robetta) Overall Quality Factor 98.063 66.97% of the residues have averaged 3D-1D score >= 0.2 Fail Buried outlier protein atoms total from 1 Model: 4.2% warning 12 345678 91011 12 131415161718 19 20 2122232425 2627 28 29 303132 33 3435 363738 39 40 41 42 43 4445 46 Out of 8 evaluations Errors: 2 Warning: 1 Pass: 5 97.3% 4. Discussion P. aeruginosa PA01 is an omnipresent pathogenic bacterium that can cause acute and chronic infection to humans by contaminating environmental water and food, daily food spoilage, and infections. It is a rising concern for its increasing resistance against a broad range of antimicrobials. The biofilm-forming ability and evolution of antibiotic tolerance shapes pseudomonas isolate highly resistant against imipenem (95.3%), trimethoprim-sulfamethoxazole (69.8%), aztreonam (60.5%), chloramphenicol (45.3%), and meropenem (27.9%) [ 69 ]. Factors like chromosomal mutations and transferring of resistant genes through horizontal gene transfer contribute to its broad-spectrum drug resistance property [ 70 ]. Thus, it is necessary to introduce a new drug target when there will be noticed multi-drug resistance for any diseases or problem. In silico process has a great advantage for the identification of new drug targets in that situation within a very short time. Consequently, to combat the ever-increasing danger of antibiotic resistance, identifying novel drug targets is a dire necessity. A drug target should have some properties before it is considered as a new target which includes being non-homologous to human proteome, human anti-targets, human gut microbiota, having virulence capability, having druggability, and so on. For this reason, we scrutinized the properties of our targeted essential hypothetical proteins where analyzing these EHPs from multidrug resistance bacteria can lead to the identification of new potential therapeutic solutions. We searched for essential hypothetical proteins (EHPs) among the 336 essential proteins of this bacterial strain to meet this need. Essential genes/proteins are those that are vital for a pathogen's survival and thereby analyzing their functions and metabolic pathways, crucial information that may be central to life can be retrieved. In this research, we discovered 18 EHPs for the first time that may provide valuable information about the pathogenesis, molecular mechanisms, and functions of this bacteria. Functional annotation is a prerequisite in understanding the pathogen metabolic pathways and the products that they synthesize for their survival in adverse conditions. Moreover, domain analysis, which is a basic, distinctive, and stable unit of a protein structure that is fiercely conserved during the evolutionary process, is crucial for further investigation [ 71 ]. Moreover, the function of a protein is directly or indirectly related to the subcellular localization [ 72 ]. The physicochemical properties of a protein depict a chemical assessment that shows the identity of chemical nature, physical hazards and to understand or predict molecular attributes. The combined analysis of the physicochemical properties helps to characterize the proteins annotated as hypothetical proteins from the genome of an opportunistic pathogen like P. aeruginosa PAO1(Table 3 ) [ 73 ]. In this study, the PPI network has provided congruent meaningful insights into the protein’s function. Here, we have looked for potential relativity to our predicted function of EHPs and their connectivity with proteins involved with functionally important activities. The protein PA2986 (NP_251676.1) related to the MacB-like periplasmic core domain, represents a connection with 45 proteins of which 8 are hypothetical proteins (HP). A notable number of proteins grouped with PA2986 are involved in protein translocation activities such as translocation protein TolQ, TolR and TolB. Tol proteins show activity in gram-negative bacteria by providing stability to the outer membrane [ 74 ]. Moreover, ABC transporter ATP-binding protein (PA0073) is related to it as it functions by utilizing TolC exit duct by shifting substrates to extracellular space from the periplasm [ 75 ]. This finding supports the idea of PA2986 being a member of the MacB-like periplasmic core domain. Another important protein for bacterial survival lysS, a lysine tRNA ligase was found to interact with PA2986 which is a mutant in some gram-negative bacteria conferring resistance against the OP0595, diazabicyclooctane b-lactamase inhibitor (an antibiotic) [ 76 ]. We have found Penicillin-Binding Protein 1 called ponA protein in this group of networks. Alteration in the ponA protein ( penicillin-binding protein 1A) has a significant role in harnessing Chromosomally mediated resistance against penicillin in N. gonorrhoeae [ 77 ]. Similarly, other considerable proteins like outer-membrane lipoprotein carrier protein lolA, transporter ExbB, penicillin-binding protein 1A (ponA) interacted with PA2986 Fig. 3 . PA0759 (NP_249450.1) has got the 2nd largest degree value having 41 nodes (Table 4 ) in connection of which 17 are HPs. The highest betweenness centrality value of 17881.76 determines its significance towards cell signaling pathways as in the case of directed or regulated networks, Betweenness centrality is considered to be a much robust essentiality indicator than degree value [ 78 ]. Genes in this hub include proteins having prime roles in translational regulation and cellular metabolic activities such as glycine cleavage system protein T2 [ 79 ], translation elongation factor (tsf) [ 80 ], and ribosomal large subunit pseudouridine synthase C (rluC) [ 81 ], respectively. Moreover, RecO protein in this network is a replication repairing protein from the RecF recombination repair pathway that facilitates both DNA strand annealing and DNA recombination in complex with RecA protein found in high radiation tolerant bacteria Deinococcus radiodurans [ 82 ]. This property may also contribute to better survival efficacy for P. aeruginosa PA01 in extreme conditions. The PA4562 (NP_253252.1) protein is a probable member of the Lipid II flippase MurJ family which is used for the genesis of lipid II on both inner and outer leaflets and that ultimately produces peptidoglycan in almost every bacterial species. Peptidoglycan is the primary protective foundation for shielding against environmental hazards and is involved in cell wall organizations [ 83 ]. Proteins related with morphological importance in bacteria such as flagellar basal body rod protein ( FlgC) [ 84 ], rod shape-determining protein (rodA) [ 85 ], type 4 fimbrial biogenesis outer membrane protein (PilQ) [ 86 ] are present in a connection with the PA4562 proteins that strengthen our prediction regarding this protein function. The proteins PA3685 (NP_252375.1) and PA3767 (NP_252456.1) are adjoined with 12 and 6 HPs respectively. Both of these proteins are largely involved with enzymes of different molecular functions-tRNA N6-adenosine threonyl carbamoyl transferase (gcp) is a universal structural modifier found at position 37 of tRNAs that provides the anticodon loop with greater binding efficiency to ribosomes invitro in E. coli [ 87 ]. The protein UDP-2,3-diacyl glucosamine hydrolase (PA1792) is hypothesized to be catalyzing lipid-A biogenesis in E. coli bacteria [ 88 ]. Lipid-A is a saccharolipid that modulates lipopolysaccharide (LPS) anchorage on the outer leaflet of the outer membrane in gram-negative bacteria which is an essential component for the bacteria shielding from antibiotics and sustain its viability [ 89 ]. Besides other proteins having enzymatic properties include thiamine monophosphate kinase (thiL), ATP-dependent DNA helicase DinG (PA1045), riboflavin-specific deaminase/reductase (ribD), amidotransferase(PA1742), acetyltransferase (PA2631) Fig. 3 . The majority of the proteins connected with PA4992 (NP_253679.1) from aldo/keto reductase family are uncharacterized proteins. The PA4167 protein has a contributing role as a source of carbon and energy for a large number of bacteria [ 90 ]. The hub proteins PA4093 (NP_252782.1) and PA4992 (NP_253679.1) are interconnected through the intermediate protein PA4098, a probable short-chain dehydrogenase enzyme [ 91 ]. The mgtE is an Mg transporter that represents a connection with the PA3481 (NP_252171.1) which can be an opportunistic inhibitor for the type III secretion system (T3SS). T3SS is a formidable toxin injected by P. aeruginosa that can ultimately cause cell death into its host. The mgtE interrupts with the T3SS transcription regulation system by provoking rsmYZ gene transcription hence inhibits T3SS protein expression [ 92 ]. Interestingly another analogous protein, DNA polymerase II (polB) is associated with this same hub protein PA3481. PolB functions as a crucial candidate for repressing the translation process of master T3SS regulator ExsA. ExsA operates a major role in maintaining the regulatory cascade of T3SS. Thus affecting ExsA expression can prohibit T3SS toxin secretion process. Furthermore, S Chakravarty et al. , found that T3SS transcription is attenuated when polB is overexpressed. Therefore, polB may act as a promising target for therapeutic interventions [ 93 ]. Besides, proteins responsible for exopolysaccharide biosynthesis and biofilm formation namely pslA [ 94 ] and pslD [ 95 ] are both members of the psl operon. The presence of such virulent protein types in this protein hub suggests PA3481 as a crucial protein involved in multiple virulence pathways in P. aeruginosa . The PA4636 (NP_253326.1) protein harbors some of the virulent proteins like lptA and algQ that is required for the biogenesis of lipid bilayer in the outer membrane in P. aeruginosa [ 96 ] and facilitates in developing a chronic infection in cystic fibrosis [ 97 ]. Some notable mutual interactions also have been observed between two hub proteins like PA2986 and PA4562 where interrelated proteins include – mraY, a potential target for antibiotic development is a crucial element for the bacterial cell wall synthesis [ 98 ]; opr86, an outer membrane protein found previously in all gram-negative bacteria. Likewise suggested as a potential drug target with significant therapeutic potential against P. aeruginosa in earlier studies [ 99 ]; rpoH, a 32-kDa heat shock protein in E. coli can also take part as a complementary for sigma factor during the increasing temperature in the environment as well as while starving [ 100 ]; PA5568 possess an inner membrane translocation subunit protein YidC which facilitates proteins to be passed onto inner membranes without the help of Sec translocase complex proteins [ 101 ]; ComL is a lipoprotein that facilitates the DNA transformation process in N. gonorrhoeae [ 102 ]. Lastly, organic solvent tolerance protein OstA holds interaction simultaneously with the top 3 hub genes of maximum node connection. Concurrently, OstA is a protein of high molecular significance as it is found in almost all gram-negative bacteria and is involved in the bacterial envelope biogenesis process. A study by HC Chiu et al. , found that OstA deficiency in Helicobacter pylori causes sensitivity to organic solvent, impaired membrane permeability, and vulnerability to antibiotics [ 103 ]. The function of the proteins in this network shows relational integrity with our predicted HPs. Knowing the protein’s function in a protein-protein interaction network can facilitate the process of discovering the proteins with unknown functions [ 104 ]. Herein, we analyzed these properties of our targeted essential hypothetical proteins where PBIT servers direct categorized all the non- homology features (Table 5 ). We have selected NP_249450.1 and NP_251676.1 respectively for being virulent determined by two of our tools with strong confidence scores. Targeting these virulent factors can limit the pathogenicity of P. aeruginosa . Even antivirulence drugs insist a pathogen towards a weaker selection for resistance in them compared to antibiotics [ 11 ]. Therefore, understanding the virulence factors and their role in pathogenesis can lead us to a new potential therapeutic solution. Besides, druggability analysis also confirmed that NP_249450.1 and NP_251676.1 can be a new and potential drug targets. Structure prediction and quality assessment of the predicted structure are also parallelly important to evaluate the molecular and biological functions of a protein in cells for in-depth analysis and drug target identification [ 105 ]. Before structure prediction, the information of Alpha helix (Hh), Extended strand (Ee), Beta turn (Tt), or Random coil (Cc) helps to establish the secondary structure. That’s why the secondary structure was annotated to complete the structure related to all the information of our selected two proteins (Fig. 4 , Fig. 5 , and supplementary table 3 ). Thus, we further predicted the 3D structure of two EHPs (NP_249450.1 and NP_251676.1) and assessed the quality of these structures to decipher their unique conformation. The 3D structure and Ramachandran plot are depicted in Fig. 6 and Fig. 7 , respectively. The predicted structure's quality assessment parameters are listed in Table 6 , and the overall quality factor is shown in Supplementary Fig. 1 . Our predicted structure is accurate and reliable, according to Ramachandran plot analysis, since more than 90% of residues are considered the cutoff value and our findings surpass that range by more than 94%. Therefore, this structural and functional information will open a new window for further identification of potential drug candidates that can halt the surge of this pathogenic bacterium from becoming resistant. 5. Conclusion Unveiling the functional characterization of pathogenic microorganisms is of great importance in biological processes and medical science. Essential proteins and essential hypothetical proteins are versatile macromolecules that can be crucial in inferring new treatment strategies towards these pathogenic bacteria. For functional characterization of EHPs, we used an in-silico approach in combination with different bioinformatics databases/tools, with ROC analysis indicating that these tools are highly reliable for functional characterization of P. aeruginosa PA01. We attributed function to 18 EHPs and analyzed subcellular localization and physiochemical properties of these proteins. Afterward, a PPIs network analysis was carried out on 9 stable EHPs and their functionally related proteins from this bacterium. Further, host non-homologous analysis predicts 5 pathogen-specific proteins, three of which have virulent factors that could be used as novel therapeutic targets. Finally, the structural conformation of two EHPs was determined, and the accuracy of the predicted model was evaluated, indicating that this model is highly accurate. Our findings will pave the way for new antibacterial drugs and treatment strategies to be developed by focusing on these novel drug targets. Declarations Funding There was no significant funding support for this investigation. Conflict of interest The authors declare that there is no conflict of interest. References Klockgether J, Tümmler B (2017) Recent advances in understanding Pseudomonas aeruginosa as a pathogen. F1000Research, p 6 Diggle SP, Whiteley M, Profile M (2020) Pseudomonas aeruginosa: opportunistic pathogen and lab rat. Microbiology 166(1):30 Nikolaidis M, Mossialos D, Oliver SG et al (2020) Comparative Analysis of the Core Proteomes among the Pseudomonas Major Evolutionary Groups Reveals Species-Specific Adaptations for Pseudomonas aeruginosa and Pseudomonas chlororaphis. Diversity 12(8):289 Stover CK, Pham XQ, Erwin A et al (2000) Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen. Nature 406(6799):959–964 Pachori P, Gothalwal R, Gandhi P (2019) Emergence of antibiotic resistance Pseudomonas aeruginosa in intensive care unit; a critical review, vol 6. Genes & diseases, pp 109–119. 2 Dubern JF, Cigana C, De Simone M et al (2015) Integrated whole-genome screening for P seudomonas aeruginosa virulence genes using multiple disease models reveals that pathogenicity is host specific. Environ Microbiol 17(11):4379–4393 Azam MW, Khan AU (2019) Updates on the pathogenicity status of Pseudomonas aeruginosa. Drug discovery today. 24:350–3591 Periasamy S, Nair HA, Lee KW et al (2015) Pseudomonas aeruginosa PAO1 exopolysaccharides are important for mixed species biofilm community development and stress tolerance. Front Microbiol 6:851 Schleheck D, Barraud N, Klebensberger J et al (2009) Pseudomonas aeruginosa PAO1 preferentially grows as aggregates in liquid batch cultures and disperses upon starvation. PLoS ONE 4(5):e5513 Klockgether J, Cramer N, Wiehlmann L et al (2011) Pseudomonas aeruginosa genomic structure and diversity. Front Microbiol 2:150 Prava J, Pranavathiyani G, Pan A (2018) Functional assignment for essential hypothetical proteins of Staphylococcus aureus N315. Int J Biol Macromol 108:765–774 Luo H, Lin Y, Gao F et al (2014) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 42(D1):D574–D580 Araujo FA, Barh D, Silva A et al (2018) GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Scientific reports. 8:1–41 Geer LY, Domrachev M, Lipman DJ et al (2002) CDART: protein homology by domain architecture. Genome Res 12(10):1619–1623 Letunic I, Khedkar S, Bork P (2021) SMART: recent updates, new developments and status in 2020. Nucleic acids research. 49:D458–D460D1 Gough J, Karplus K, Hughey R et al (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 313(4):903–919 Mistry J, Chuguransky S, Williams L et al (2021) Pfam: The protein families database in 2021. Nucleic acids research. 49:D412–D419D1 Li YH, Xu JY, Tao L et al (2016) SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS ONE 11(8):e0155290 Sillitoe I, Bordin N, Dawson N et al (2021) CATH: increased structural coverage of functional space. Nucleic Acids Res 49(D1):D266–D273 Blum M, Chang H-Y, Chuguransky S et al (2021) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49(D1):D344–D354 Gabler F, Nam SZ, Till S et al (2020) Protein Sequence Analysis Using the MPI Bioinformatics Toolkit. Curr Protocols Bioinf 72(1):e108 Koskinen P, Törönen P, Nokso-Koivisto J et al (2015) PANNZER: high-throughput functional annotation of uncharacterized proteins in an error-prone environment. Bioinformatics 31(10):1544–1552 Hawkins T, Chitale M, Luban S et al (2009) PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data, vol 74. Structure, Function, and Bioinformatics, Proteins, pp 566–582. 3 Chitale M, Hawkins T, Park C et al (2009) ESG: extended similarity group method for automated protein function prediction. Bioinformatics 25(14):1739–1745 Yu NY, Wagner JR, Laird MR et al (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26(13):1608–1615 Yu CS, Lin CJ, Hwang JK (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n‐peptide compositions. Protein science. 13:1402–14065 Yu CS, Chen YC, Lu CH et al (2006) Prediction of protein subcellular localization. Proteins Struct Funct Bioinform 64(3):643–651 Krogh A, Larsson B, Von Heijne G et al (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567–580 Käll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338(5):1027–1036 Tusnady GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17(9):849–850 Dobson L, Reményi I, Tusnády GE (2015) CCTOP: a Consensus Constrained TOPology prediction web server. Nucleic Acids Res 43(W1):W408–W412 Omasits U, Ahrens CH, Müller S et al (2014) Protter: interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 30(6):884–886 Nielsen H Predicting secretory proteins with SignalP , in Protein function prediction 2017,Springer. p.59–73 Hiller K, Grote A, Scheer M et al (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res 32(suppl2):W375–W379 Ganapathiraju M, Balakrishnan N, Reddy R et al (2008) Transmembrane helix prediction using amino acid property features and latent semantic analysis. Bmc Bioinformatics. Springer Gasteiger E, Gattiker A, Hoogland C et al (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31(13):3784–3788 Shahbaaz M, ImtaiyazHassan M, Ahmad F (2013) Functional annotation of conserved hypothetical proteins from Haemophilus influenzae Rd KW20. PloS one. 8:e8426312 Naqvi AAT, Shahbaaz M, Ahmad F et al (2015) Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum PloS one 10(4):e0124177 Zhou G, Soufan O, Ewald J et al (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47(W1):W234–W241 Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504 Shende G, Haldankar H, Barai RS et al (2017) Pipeline Builder for Identification of drug Targets for infectious diseases. Bioinformatics 33(6):929–931 Valdes AM, Walter J, Segal E et al (2018) Role of the gut microbiota in nutrition and health.Bmj,361 Saha S, Raghava G (2006) VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition, vol 4. Genomics, proteomics & bioinformatics, pp 42–47. 1 Garg A, Gupta D (2008) VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 9(1):1–12 Gupta A, Kapil R, Dhakan DB et al (2014) MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. PLoS ONE 9(4):e93907 Emmerich CH, Gamboa LM, Hofmann MC et al (2020) Improving target assessment in biomedical research: the GOT-IT recommendations.Nature Reviews Drug Discovery, : p.1–18 Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082 Geourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681–684 Buchan DW, Jones DT (2019) The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res 47(W1):W402–W407 Waterhouse A, Bertoni M, Bienert S et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303 Yang J, Anishchenko I, Park H et al (2020) Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences, 117(3): p. 1496–1503 HeeShin W (2014) Prediction of protein structure and interaction by GALAXY protein modeling programs. Biodesign 2:1–11 Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519 Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170 Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83–85 Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264(1):121–136 Hooft RW, Vriend G, Sander C et al (1996) Errors in protein structures. Nature 381(6580):272–272 Laskowski RA, MacArthur MW, Moss DS et al (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291 Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition. 30:1145–11597 Eng J (2017) ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University [updated 2014 March 19] , Accessed 25/05/17. Available from: http://www . jrocfit. org Kuk AC, Mashalidis EH, Lee S-Y (2017) Crystal structure of the MOP flippase MurJ in an inward-facing conformation. Nat Struct Mol Biol 24(2):171–176 Islam MS, Shahik SM, Sohel M et al (2015) In silico structural and functional annotation of hypothetical proteins of Vibrio cholerae O139, vol 13. Genomics & informatics, p 53. 2 Malhotra H, Kaur H (2016) A bioinformatics approach for functional and structural analysis of hypothetical proteins of Clostridium difficile. Imp J Interdiscip Res 2:1601–1609 Ikai A (1980) Thermostability and aliphatic index of globular proteins. J Biochem 88(6):1895–1898 da Costa WLO, Araújo CLdA, Dias LM et al (2018) Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance. PLoS ONE 13(6):e0198965 Guruprasad K, Reddy BB, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng Des Selection 4(2):155–161 Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105–132 Xia J, Benner MJ, Hancock RE (2014) NetworkAnalyst-integrative approaches for protein–protein interaction network analysis and visual exploration. Nucleic Acids Res 42(W1):W167–W174 Meng L, Liu H, Lan T et al (2020) Antibiotic Resistance Patterns of Pseudomonas spp. Isolated From Raw Milk Revealed by Whole Genome Sequencing. Front Microbiol 11:1005 Poole K (2011) Pseudomonas aeruginosa: resistance to the max. Front Microbiol 2:65 Rahman A, Susmi TF, Yasmin F et al (2020) Functional annotation of an ecologically important protein from Chloroflexus aurantiacus involved in polyhydroxyalkanoates (PHA) biosynthetic pathway. SN Appl Sci 2(11):1–13 Itzhak DN, Tyanova S, Cox J et al (2016) Global, quantitative and dynamic mapping of protein subcellular localization. elife. 5:e16950 Samsonov GV (2012) Handbook of the Physicochemical Properties of the Elements. Springer Science & Business Media Lazzaroni J-C, Dubuisson J-F, Vianney A (2002) The Tol proteins of Escherichia coli and their involvement in the translocation of group A colicins, vol 84. Biochimie, pp 391–397. 5–6 Crow A, Greene NP, Kaplan E et al (2017) Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily. Proceedings of the National Academy of Sciences, 114(47): p. 12572–12577 Doumith M, Mushtaq S, Livermore D et al (2016) New insights into the regulatory pathways associated with the activation of the stringent response in bacterial resistance to the PBP2-targeted antibiotics, mecillinam and OP0595/RG6080. J Antimicrob Chemother 71(10):2810–2814 Ropp PA, Hu M, Olesky M et al (2002) Mutations in ponA, the gene encoding penicillin-binding protein 1, and a novel locus, penC, are required for high-level chromosomally mediated penicillin resistance in Neisseria gonorrhoeae. Antimicrob Agents Chemother 46(3):769–777 Yu H, Kim PM, Sprecher E et al (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3(4):e59 Müller M, Papadopoulou B (2010) Stage-specific expression of the glycine cleavage complex subunits in Leishmania infantum. Molecular and biochemical parasitology. 170:17–271 An G, Bendiak DS, Mamelak LA et al (1981) Organization and nucleotide sequence of a new ribosomal operon in Escherichia coli containing the genes for ribosomal protein S2 and elongation factor Ts, vol 9. Nucleic acids research, pp 4163–4172. 16 Conrad J, Sun D, Englund N et al (1998) The rluC gene of Escherichia coli codes for a pseudouridine synthase that is solely responsible for synthesis of pseudouridine at positions 955, 2504, and 2580 in 23 S ribosomal RNA. J Biol Chem 273(29):18562–18566 Makharashvili N, Koroleva O, Bera S et al (2004) A novel structure of DNA repair protein RecO from Deinococcus radiodurans. Structure 12(10):1881–1889 Zheng S, Sham L-T, Rubino FA et al (2018) Structure and mutagenic analysis of the lipid II flippase MurJ from Escherichia coli. Proceedings of the National Academy of Sciences, 115(26): p. 6709–6714 Zuberi AR, Ying C, Bischoff DS et al (1991) Gene-protein relationships in the flagellar hook-basal body complex of Bacillus subtilis: sequences of the flgB, flgC, flgG, fliE and fliF genes. Gene 101(1):23–31 Matsuzawa H, Asoh S, Kunai K et al (1989) Nucleotide sequence of the rodA gene, responsible for the rod shape of Escherichia coli: rodA and the pbpA gene, encoding penicillin-binding protein 2, constitute the rodA operon. J Bacteriol 171(1):558–560 Martin PR, Hobbs M, Free PD et al (1993) Characterization of pilQ, a new gene required for the biogenesis of type 4 fimbriae in Pseudomonas aeruginosa. Molecular microbiology. 9:857–8684 El Yacoubi B, Lyons B, Cruz Y et al (2009) The universal YrdC/Sua5 family is required for the formation of threonylcarbamoyladenosine in tRNA. Nucleic Acids Res 37(9):2894–2909 Babinski KJ (2004) Genetic and biochemical characterization of the specific UDP-2, 3-diacylglucosamine hydrolase of lipid A biosynthesis. Metzger LE, Lee JK, Stroud RM et al (2010) Discovery, characterization, and structural determination of a novel UDP-2, 3‐diacylglucosamine hydrolase. Wiley Online Library Yum D-Y, Lee B-Y, Pan J-G (1999) Identification of the yqhE andyafB Genes Encoding Two 2, 5-Diketo-d-Gluconate Reductases inEscherichia coli. Appl Environ Microbiol 65(8):3341–3346 Moynie L, Schnell R, McMahon SA et al (2013) The AEROPATH project targeting Pseudomonas aeruginosa: crystallographic studies for assessment of potential targets in early-stage drug discovery. Acta Crystallographica Section F: Structural Biology and Crystallization Communications, 69(1): p. 25–34 Chakravarty S, Melton CN, Bailin A et al (2017) Pseudomonas aeruginosa magnesium transporter MgtE inhibits type III secretion system gene expression by stimulating rsmYZ transcription.Journal of bacteriology,199(23) Chakravarty S, Ramos-Hegazy L, Gasparovic A et al (2020) DNA alternate polymerase PolB mediates inhibition of type III secretion in Pseudomonas aeruginosa.Microbes and Infection, : p.104777 Overhage J, Schemionek M, Webb JS et al (2005) Expression of the psl operon in Pseudomonas aeruginosa PAO1 biofilms: PslA performs an essential function in biofilm formation. Appl Environ Microbiol 71(8):4407–4413 Campisano A, Schroeder C, Schemionek M et al (2006) PslD is a secreted protein required for biofilm formation by Pseudomonas aeruginosa. Appl Environ Microbiol 72(4):3066–3068 Sperandeo P, Cescutti R, Villa R et al (2007) Characterization of lptA and lptB, two essential genes implicated in lipopolysaccharide transport to the outer membrane of Escherichia coli. J Bacteriol 189(1):244–253 Konyecsni W, Deretic V (1990) DNA sequence and expression analysis of algP and algQ, components of the multigene system transcriptionally regulating mucoidy in Pseudomonas aeruginosa: algP contains multiple direct repeats. J Bacteriol 172(5):2511–2520 Chung BC, Zhao J, Gillespie RA et al (2013) Crystal structure of MraY, an essential membrane enzyme for bacterial cell wall synthesis. Science 341(6149):1012–1016 Tashiro Y, Nomura N, Nakao R et al (2008) Opr86 is essential for viability and is a potential candidate for a protective antigen against biofilm formation by Pseudomonas aeruginosa. J Bacteriol 190(11):3969–3978 Jenkins DE, Auger EA, Matin A (1991) Role of RpoH, a heat shock regulator protein, in Escherichia coli carbon starvation protein synthesis and survival. J Bacteriol 173(6):1992–1996 Samuelson JC, Chen M, Jiang F et al (2000) YidC mediates membrane protein insertion in bacteria. Nature 406(6796):637–641 Fussenegger M, Facius D, Meier J et al (1996) A novel peptidoglycan-linked lipoprotein (ComL) that functions in natural transformation competence of Neisseria gonorrhoeae. Molecular microbiology. 19:1095–11055 Chiu HC, Lin TL, Wang JT (2007) Identification and characterization of an organic solvent tolerance gene in Helicobacter pylori. Helicobacter 12(1):74–81 Rao VS, Srinivas K, Sujini G et al (2014) Protein-protein interaction detection: methods and analysis. International journal of proteomics, 2014 Imam N, Alam A, Ali R et al (2019) In silico characterization of hypothetical proteins from Orientia tsutsugamushi str. Karp uncovers virulence genes. Heliyon 5(10):e02734 Supplementary Files SupplementaryFile1.xlsx Supplementary File 1: ROC analysis SupplementaryFigure1.tif Supplementary Figure 01: Overall quality factor of ERRAT value from SAVES v6.0 server. (a) the quality factor is 87.6325% for the NP_249450.1 protein structure from SWISS-MODEL, (b) the quality factor is 93.3649% for NP_251676.1 protein structure from SWISS-MODEL, (c) the quality factor is 92.459% for the NP_249450.1 protein structure from Robetta, (d) the quality factor is 98.063% for NP_251676.1 protein structure from Robetta. SupplementaryTable1.docx Supplementary Table 1: Subcellular localization and transmembrane topology SupplementaryTable2.docx Supplementary Table 2: List of functions of the proteins found in PPI network SupplementaryTable3.docx Supplementary Table 3: Properties of secondary structure from SOPMA database. 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Takim Sarker","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Takim","lastName":"Sarker","suffix":""},{"id":105678220,"identity":"4d030344-f89b-41da-829c-3e56ea7dc98c","order_by":2,"name":"Md Ashiqul Islam","email":"","orcid":"","institution":"University of Windsor","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md","middleName":"Ashiqul","lastName":"Islam","suffix":""},{"id":105678221,"identity":"0381e2b8-b014-41d9-93c3-172ff7456093","order_by":3,"name":"Mohammad Uzzal Hossain","email":"","orcid":"","institution":"National Institute of Biotechnology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Uzzal","lastName":"Hossain","suffix":""},{"id":105678222,"identity":"bc16c916-1338-422b-a154-fd761be5a540","order_by":4,"name":"Mahmudul Hasan","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahmudul","middleName":"","lastName":"Hasan","suffix":""},{"id":105678223,"identity":"9829e572-2a96-44b1-b993-06694b5a3675","order_by":5,"name":"Tasmina Ferdous Susmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYNACg/9yIOrAA2IU80C0MBuDtSQQr4WBObEBRBGlxZ7/jPGnGwVs6fPDDj8E2mInp9tAyBaJHDPpHAOe3I230wyAWpKNzQ4Q1MJjxpxjIJG7cXYCSMuBxG0EtQAd9jnHwCDdcHb6ByK1MOQYAB2WkCAPJInUciOtDKTYcIN0TsGBBAMi/MLef3jz55w/B+TlZ6dv/vChwk6OoBY4MACrNCBWOQjIN5CiehSMglEwCkYUAAD3LECb4zddUgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2628-2371","institution":"Jashore University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tasmina","middleName":"Ferdous","lastName":"Susmi","suffix":""}],"badges":[],"createdAt":"2022-05-12 19:05:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1650735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1650735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21925200,"identity":"4678e9f6-b73e-426c-afdc-4fd84f7abe4d","added_by":"auto","created_at":"2022-05-26 15:52:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSchematic representation of the whole methodology used in our investigation\u003c/em\u003e.\u003c/strong\u003e There are four segments, \u003cstrong\u003eSegment I:\u003c/strong\u003e Functional annotation and properties characterization; \u003cstrong\u003eSegment II:\u003c/strong\u003e Protein-protein interaction network; \u003cstrong\u003eSegment III:\u003c/strong\u003e Non-homology analysis, virulence factor prediction and druggability identification; \u003cstrong\u003eSegment IV:\u003c/strong\u003e Structure prediction and structure validation.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/be21520fccf3170bfe9c4b5b.png"},{"id":21925762,"identity":"487b3488-856c-454c-a5bf-0bde1da9addd","added_by":"auto","created_at":"2022-05-26 15:57:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe subcellular localization of 18 EHPs is displayed by the column plot\u003c/em\u003e.\u003c/strong\u003e Five categories of columns are for 5 types of subcellular localization (cytoplasmic, cytoplasmic membrane, inner membrane, periplasmic and unknown). Here lighter black represents the data from CELLO database and the color grey is for the database PSORTb.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/50bc2d26b44b0eaedbf98838.png"},{"id":21927733,"identity":"a9879eab-2fb0-4b14-8dbb-b76a7002c050","added_by":"auto","created_at":"2022-05-26 16:07:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProtein-protein interaction network of 9 stable EHPs collected from P. aeruginosa PAO1 Protein Interactome database\u003c/em\u003e.\u003c/strong\u003e The network has 261 nodes and 269 edges provided with 11 subnetworks (Hubs) with a minimum of 3 nodes each. The nodes with lower degree values are colored green namely PA4992, PA3481, PA4093, PA4636. The color gradually turned into deep purple by the increase of node degree values. The nodes in cyan blue meaning 2 or more interactions with their corresponding subnetworks.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/95edc6db10e9e0ecc1f1ec3a.png"},{"id":21925765,"identity":"006e1828-d364-41de-87b7-1868f8b554b3","added_by":"auto","created_at":"2022-05-26 15:57:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":582146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe secondary structure of NP_249450.1 from (a) PSIPRED website, and (b) SOPMA websites.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/ee1eb6e3dbda287c193f983c.png"},{"id":21925199,"identity":"3c24cd6a-6717-49af-bbaf-31f02b9b0252","added_by":"auto","created_at":"2022-05-26 15:52:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":542323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe secondary structure of NP_251676.1 from (a) PSIPRED server, and (b) SOPMA database.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/32b8643e8d07b0b3e6d4bb52.png"},{"id":21925203,"identity":"844f0cd9-aa57-45f3-b8a0-72aef6072635","added_by":"auto","created_at":"2022-05-26 15:52:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":624833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThree-dimen\u003c/em\u003esional\u003cem\u003e homolopgy modeling. \u003c/em\u003e\u003c/strong\u003e(a) template-based homology modelling structure of NP_249450.1 from SWISS-MODEL, (b) \u003cem\u003eab-initio\u003c/em\u003e modelling structure of NP_249450.1 from the Robetta server, (c) template-based homology modelling structure of NP_251676.1 from SWISS-MODEL, and (d) \u003cem\u003eab-initio\u003c/em\u003e modelling structure of NP_251676.1 from the Robetta server.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/6c48f26dd06c05c69fe970a5.png"},{"id":21926796,"identity":"b064d1fb-34df-4f6b-83ce-f98c1f97da30","added_by":"auto","created_at":"2022-05-26 16:02:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":473530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThree-dimensional structure assessment by Ramachandran plot analysis by PROCHECK.\u003c/em\u003e\u003c/strong\u003e (a) Ramachandran plot of NP_249450.1 protein from SWISS-MODEL structure; (b) Ramachandran plot of NP_249450.1 protein from Robetta model; (c) Ramachandran plot of NP_251676.1 protein from SWISS-MODEL structure; (d) Ramachandran plot of NP_251676.1 protein from Robetta model.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/224058265f21636660763082.png"},{"id":22096824,"identity":"537e18f1-f7dd-4229-9a57-8c4374bb1488","added_by":"auto","created_at":"2022-05-31 20:04:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3501115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/07912064-a75a-49d5-b162-5157a97b2190.pdf"},{"id":21926795,"identity":"23128c02-5fdd-4e69-8b99-83e095a6f70e","added_by":"auto","created_at":"2022-05-26 16:02:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":125469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary File 1:\u003c/strong\u003e ROC analysis\u003c/p\u003e","description":"","filename":"SupplementaryFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/48bb28633a4f29e51be69c0c.xlsx"},{"id":21925206,"identity":"8e5db5ed-541e-4037-ae03-1f8b28af5c37","added_by":"auto","created_at":"2022-05-26 15:52:00","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1531256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 01\u003c/strong\u003e: \u003cstrong\u003e\u003cem\u003eOverall quality factor of ERRAT value from SAVES v6.0 server.\u003c/em\u003e\u003c/strong\u003e (a) the quality factor is 87.6325% for the NP_249450.1 protein structure from SWISS-MODEL, (b) the quality factor is 93.3649% for NP_251676.1 protein structure from SWISS-MODEL, (c) the quality factor is 92.459% for the NP_249450.1 protein structure from Robetta, (d) the quality factor is 98.063% for NP_251676.1 protein structure from Robetta.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/3695a813fdb5162e19d72838.tif"},{"id":21925764,"identity":"799e0468-2954-4a6f-9d3f-fe79123a813e","added_by":"auto","created_at":"2022-05-26 15:57:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1: \u003c/strong\u003eSubcellular localization and transmembrane topology\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/014dc30bcddeb42f09caf049.docx"},{"id":21928313,"identity":"da72b609-f0e5-4444-82c8-3a712042884e","added_by":"auto","created_at":"2022-05-26 16:12:00","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2:\u003c/strong\u003e List of functions of the proteins found in PPI network\u003c/p\u003e","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/1c00581fa112fe4ca33e0b68.docx"},{"id":21925768,"identity":"870f51a5-af43-41c9-ae07-54374f4fe653","added_by":"auto","created_at":"2022-05-26 15:57:00","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3:\u003c/strong\u003e Properties of secondary structure from SOPMA database.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-1650735/v1/fd31f5de38643415f0dfd9bf.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eTargeting Essential Hypothetical Proteins of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PAO1 for Mining of Novel Therapeutics: An \u003cem\u003ein silico\u003c/em\u003e Approach\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e often termed as an opportunistic pathogen, is a rod-shaped, motile, gram-negative and non-fermenting bacteria found ubiquitously in soil and water as well as found in colonies on the animate part of plant and animal including humans [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Isolates collected from diverse environments reported 272 species of the \u003cem\u003ePseudomonas\u003c/em\u003e genus in which \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 is one of the most commonly used laboratory strains as well as employed to generate publicly accessible genomic resources [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 is the first-ever strain of its species having a completely sequenced genome from a chronic lesion isolate dated from the 1950s. The genome is 6.3 Mbp long that includes 5570 ORFs, roughly 89.4% coding regions, and 0.4% stable RNAs. This was the largest bacterial genome available during the year 2000 when sequenced. However, despite the same species, different genomic and phenotypic changes are found across isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 strains stored in different laboratories worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA broad spectrum of host targets including nematodes, insects, plants, and mammals are susceptible to infection by \u003cem\u003eP. aeruginosa\u003c/em\u003e species [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is found harmless in normal gut microflora but causes dangerous infection in critically ill ICU patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This trend in pathogenesis makes them an opportunistic pathogen [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is regarded to be within the top three causative agents for infection caused by opportunistic pathogens annually in the community as well as related to (10\u0026ndash;15) % of hospital-acquired infections [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In 2015, a report from the European Antimicrobial Resistance Surveillance Network (EARS-Net) on European regions revealed that around 13.7% strains of \u003cem\u003eP. aeruginosa\u003c/em\u003e had acquired resistance to a minimum of three anti-microbial communities whereas about 5.5% of the strains were resistant against five anti-microbial groups. Every year in the USA alone, roughly 440 deaths and 51,000 infection cases are caused by \u003cem\u003eP. aeruginosa\u003c/em\u003e of which over 13% results from multi-drug resistant \u003cem\u003ePseudomonas\u003c/em\u003e strains. As a consequence, \u003cem\u003eP. aeruginosa\u003c/em\u003e has been announced as one of the greatest threats to public health amongst the 12 bacterial families from the antibiotic-resistance priority pathogens enlisted by WHO in 2017 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is also involved with some other nosocomial infections like bloodstream infection, gastrointestinal infection, and urinary tract infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This bacterium poses a devastating impact on lung disease patients with cystic fibrosis (CF). Apart from CF, it is equally deadly for individuals having compromised immune systems like AIDS, cancer, burn lesions, and eye injuries. The situation can get even worse despite having robust antibiotic medication since \u003cem\u003eP. aeruginosa\u003c/em\u003e possess a wide spectrum of resistance against antibiotics including aminoglycosides, β-lactams, and fluoroquinolones. Therefore, disease stress subsequently results in organ failure and eventually death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. aeruginosa\u003c/em\u003e adopts some survival strategy that helps them to resist environmental stressors and dodging host immune responses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Some of these survival tools include biofilm formation, enzyme promiscuity, horizontal gene transfer, and quorum sensing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is one of the well-studied strains for investigating the bacterial biofilm formation process [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Three polysaccharides, alginate, Pel, and Psl, were discovered to be important for bacterial attachment and biofilm formation in \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Over 500 regulatory genes have been recorded from the \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 genome investigation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There is still a lot to discover for a better understanding of the intracellular signaling pathways and several other regulatory mechanisms involving many proteins that are still uncharacterized. Thus, domain analysis and functional annotation of essential hypothetical proteins (EHPs) can pave the way to identify new potential targets facilitating the drug repositioning development. Since these EHPs are needed for cellular, biological, and metabolic processes, their deletion or mutation can be fatal to the species. These prospective drug targets may be crucial in the development of antimicrobial drugs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, an \u003cem\u003ein-silico\u003c/em\u003e based approach has adopted for the characterization of proteins with unknown functions via different algorithm-based tools and software. Besides, a network-based analysis was directed to find interaction with critically connected hub proteins that may control major molecular activities together. The pipeline builder was employed to analyze non-homologous proteins against humans, human anti-targets, and the proteome of the gut microbiota, as well as predict virulence factors and novel drug targets. Finally, using reliable software, the structural conformation of our protein of interest with potential druggability was predicted and assessed. Thus, our analysis mainly involves the identification of essential hypothetical proteins in \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 and can further lead to the discovery of novel proteins of therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eSequence retrieval and analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe full proteome of \u003cem\u003eP. aeruginosa\u003c/em\u003e PAO1 (strain ATCC 15692) was retrieved from the NCBI genome database. The bacterial complete genome contains 6.3\u0026nbsp;million base pairs and 5564 proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The essential genes database (DEG) is then subjected to find out the essential hypothetical proteins (EHPs) from this complete proteome list by employing a series of unique keywords [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To begin, we looked for similar hypothetical proteins where we found 2181 proteins among these 5564 proteins. Following that, we searched for the exact matches of hypothetical proteins and exact matches of conserved hypothetical proteins and found 1540 and 625 hypothetical proteins, respectively. According to the DEG database, this bacterial proteome contains 336 essential proteins (EPs). Essential proteins are those that are inevitable and adequate for a living cell to survive under ideal circumstances. Consequently, we discovered 29 essential hypothetical proteins by manual curation whose genomes were entirely conserved among the 336 EPs. The status (reviewed or unreviewed), annotation score (1\u0026ndash;5), structural and functional availability, and other factors were used to further validate these 29 EHPs from the NCBI and UniProt databases. Eventually, we excluded 11 proteins, leaving 18 essential hypothetical proteins whose FASTA sequences were used to facilitate further analysis throughout this study. The complete framework of our investigation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and all the databases/software used in this study are in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBioinformatics resources used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSerial\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eno\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eServer/\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDatabase\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVersion\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eUsing Reason\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLink\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReferences\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctional Annotation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinding essential HPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tubic.tju.edu.cn/deg/\u003c/span\u003e\u003cspan address=\"http://tubic.tju.edu.cn/deg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO FEAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFor functional\u003c/p\u003e \u003cp\u003eannotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://computationalbiology.ufpa.br/gofeat/\u003c/span\u003e\u003cspan address=\"http://computationalbiology.ufpa.br/gofeat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein Homology search Domain Architecture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentification and annotation of protein domains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://smart.embl-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://smart.embl-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPERFAMILY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFor functional annotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY/\u003c/span\u003e\u003cspan address=\"https://supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePfam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetermine protein families\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVMProt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein functional family prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bidd.group/cgi-bin/svmprot/svmprot.cgi\u003c/span\u003e\u003cspan address=\"http://bidd.group/cgi-bin/svmprot/svmprot.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein domains into superfamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cathdb.info/\u003c/span\u003e\u003cspan address=\"http://www.cathdb.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterPro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassification of protein families\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/interpro/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/interpro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHHPred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSequence similarity searching, prediction of sequence features, and sequence classification.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toolkit.tuebingen.mpg.de/tools/hhpred\u003c/span\u003e\u003cspan address=\"https://toolkit.tuebingen.mpg.de/tools/hhpred\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePANNZER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunctional annotation of uncharacterized proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ekhidna2.biocenter.helsinki.fi/sanspanz/\u003c/span\u003e\u003cspan address=\"http://ekhidna2.biocenter.helsinki.fi/sanspanz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutomated protein function\u003c/p\u003e \u003cp\u003egene ontology prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kiharalab.org/web/pfp.php\u003c/span\u003e\u003cspan address=\"https://kiharalab.org/web/pfp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein Function Prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kiharalab.org/web/esg.php\u003c/span\u003e\u003cspan address=\"https://kiharalab.org/web/esg.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubcellular Localization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsortb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubcellular Localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.psort.org/psortb/\u003c/span\u003e\u003cspan address=\"https://www.psort.org/psortb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCELLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ev.2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubcellular Localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cello.life.nctu.edu.tw/\u003c/span\u003e\u003cspan address=\"http://cello.life.nctu.edu.tw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMHMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ev. 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprediction of transmembrane helices in proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbs.dtu.dk/services/TMHMM-2.0/\u003c/span\u003e\u003cspan address=\"http://www.cbs.dtu.dk/services/TMHMM-2.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhobius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprediction of transmembrane helices in proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phobius.sbc.su.se/index.html\u003c/span\u003e\u003cspan address=\"https://phobius.sbc.su.se/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMMTOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprediction of transmembrane helices in proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.enzim.hu/hmmtop/index.php\u003c/span\u003e\u003cspan address=\"http://www.enzim.hu/hmmtop/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprediction of transmembrane helices in proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cctop.enzim.ttk.mta.hu/\u003c/span\u003e\u003cspan address=\"http://cctop.enzim.ttk.mta.hu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROTTER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epredicts the presence and location of signal peptide cleavage sites in amino acid sequences and prediction of transmembrane helices in proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wlab.ethz.ch/protter/start/\u003c/span\u003e\u003cspan address=\"https://wlab.ethz.ch/protter/start/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignalP 4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epredicts the presence and location of signal peptide cleavage sites in amino acid sequences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbs.dtu.dk/services/SignalP-4.1/\u003c/span\u003e\u003cspan address=\"http://www.cbs.dtu.dk/services/SignalP-4.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrediSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrediction of Signal peptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.predisi.de/\u003c/span\u003e\u003cspan address=\"http://www.predisi.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysicochemical Properties\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtParam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecomputation of various physical and chemical parameters for a given protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein-Protein Interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetworkAnalyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ev3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPI construction and visualization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/NetworkAnalyst/uploads/ListUploadView.xhtml\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/NetworkAnalyst/uploads/ListUploadView.xhtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Homology Analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePipeline building for non- homology analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pbit.bicnirrh.res.in/\u003c/span\u003e\u003cspan address=\"http://www.pbit.bicnirrh.res.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVirulence Factor Analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVICMpred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003efunctional classification of proteins of bacteria into virulence factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/vicmpred/index.html\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/vicmpred/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirulentPred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirulentPred\u0026nbsp;is a bacterial virulent protein prediction method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.icgeb.res.in/virulent/\u003c/span\u003e\u003cspan address=\"http://bioinfo.icgeb.res.in/virulent/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epredict pathogenic proteins in both genomic and metagenomic datasets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metagenomics.iiserb.ac.in/mp3/tutorial.php\u003c/span\u003e\u003cspan address=\"http://metagenomics.iiserb.ac.in/mp3/tutorial.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDruggability Analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrugBank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentification of information on drugs and drug targets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary Structure Analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOPMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecondary structure prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\u003c/span\u003e\u003cspan address=\"https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSIPRED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecondary structure prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinf.cs.ucl.ac.uk/psipred/\u003c/span\u003e\u003cspan address=\"http://bioinf.cs.ucl.ac.uk/psipred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3D Structure Analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWISS-MODEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein 3D structure determination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.expasy.org/\u003c/span\u003e\u003cspan address=\"https://swissmodel.expasy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobetta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein 3D structure determination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://robetta.bakerlab.org/\u003c/span\u003e\u003cspan address=\"https://robetta.bakerlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalaxy Refine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefinement of protein structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\u003c/span\u003e\u003cspan address=\"http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyMOL Software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructure visualization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003cspan address=\"https://pymol.org/2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValidation Check\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERRAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVARIFY 3D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e],\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHATCHECK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROCHECK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRamachandran Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3D structure validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://services.mbi.ucla.edu/SAVES/Ramachandran/\u003c/span\u003e\u003cspan address=\"http://services.mbi.ucla.edu/SAVES/Ramachandran/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROC Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis web page calculates a receiver operating characteristic (ROC) curve from data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html\u003c/span\u003e\u003cspan address=\"http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Segment I: Functional annotation and Properties Characterization\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.2.1 Functional annotation and domain analysis of EHPs\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe functional annotation of 18 \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e EHPs was unveiled by using numerous publicly accessible databases and tools. To gain more knowledge about the molecular functions and biological processes of the EHPs, we consider protein superfamily, family, conserved domain analysis, and Gene Ontology (GO) analysis. Using an online server GO FEAT, for the functional characterization by homology searching through multiple databases such as NCBI, Uniprot, and EMBL, a preliminary assessment was performed to see if any of the HPs were allocated a family and/or protein domain [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. After preliminary evaluation, proteins conserved domains and protein functions based on domain architecture were determined by using CDART [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] from the conserved domain database (CDD) and SMART [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], respectively. For functional analysis, SUPERFAMILY 1.75 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Pfam 34.0 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], SVMProt [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], CATH 4.3 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], InterPro 84.0 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and HHPred [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] were used to identify the protein superfamily, functional family, domain, and essential sites based on similarity. PANNZER [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], PFP [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and ESG [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] tools were used for high-throughput functional annotation of EHPs, which provided gene ontology information with z-scores as well as brief explanations of the annotated protein's functionality. These GO terms facilitate understanding a gene's molecular functions, physiological roles, and cellular mechanism, which refers to the location of the gene's product. We used default parameters for all databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Subcellular localization and Transmembrane Helices Analysis\u003c/h2\u003e \u003cp\u003eSub-cellular localization of a protein can help to infer much information about that protein\u0026rsquo;s function. In our study, we employed several databases to annotate the subcellular localization of the selected 9 EHPs which includes PSORTb [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], CELLO [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], TMHMM [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Phobius [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], HMMTOP [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], CCTOP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], PROTTER [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], SignalP 4.1 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and PrediSi [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. According to PSORTb and CELLO, the proteins were distinguished by 5 major cellular position: cytoplasmic, inner membrane, periplasmic, outer membrane and extracellular. To predict transmembrane helices, TMHMM, Phobius, HMMTOP, CCTOP and PROTTER were employed. Information of transmembrane helices location is somehow beneficial for the conformation of possible 3D structure [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Besides, it is necessary to find out signal peptides which is the N-terminal part of a protein. Mainly, they are targeted to the endoplasmic reticulum to the secretory pathway and it is considered as the way of protein localization prediction [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Signal peptides were identified by using these SignalP 4.1, PROTTER and PrediSi databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Analysis of physicochemical properties\u003c/h2\u003e \u003cp\u003eThe Expasy\u0026rsquo;s ProtParam server [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was utilized for the analysis of physicochemical properties of 18 selected essential hypothetical proteins (EHPs) which include molecular weight, theoretical pI (Isoelectric Point), Formula, the total number of positively and negatively charged residues, instability index, aliphatic index and grand average of hydropathicity (GRAVY).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Segment II: Protein-Protein Interaction Network of 9 EHPs\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Protein-protein interaction network analysis\u003c/h2\u003e \u003cp\u003eThe function of a protein molecule often is modulated by its surrounding protein networks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For this reason, it is important to discover the protein network to get an insight into the functional association of a particular protein [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, we have used NetworkAnalyst v3.0 for network building [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We have inputted a list of genes containing 9 EHPs with their Uniprot IDs (Q9HXM8, Q9HWT5, Q9HVM2, Q9HVF5, Q9I5H0, Q9HZL8, Q9HYC8, Q9HXV5, and Q9HUH3) since all of these proteins were found stable through the physicochemical analysis. The Generic PPI option under Protein-protein Interactions (PPI) was checked for further processing. \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 interactome database provided with robust computational prediction and experimentally validated data were adopted for network building. Next, the corresponding network was explored for further analysis in Cytoscape. Cytoscape is a standalone software that enables several topological parameter analyses like discovering the shortest possible path, node degree distribution, clustering hub genes of the network [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Segment III: Non-Homology Analysis, Virulence Factor Prediction and Druggability Identification\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Non-homology analysis against human proteome and human anti-targets\u003c/h2\u003e \u003cp\u003eSeveral features were needed for the identification of the drug target for any human diseases. For this reason, to analyze non-homology aspects, we tried Pipeline builder for identification of target (PBIT) server for the non-homology analysis against human proteome, against human anti-targets and human gut flora proteomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Using the pipeline builder, we first identified human homologous proteins that share high sequence similarity with human proteome. The sequence similarity of the inputted 9 sequences was figured using BLAST algorithm where E-value\u0026thinsp;\u0026gt;\u0026thinsp;0.005 and % sequence identity\u0026thinsp;\u0026lt;\u0026thinsp;50 was set. 8 of the 9 input sequences are non-homologous that were selected for further investigation. These homologous proteins were filtered to avoid the undesirable toxic-effects for these similarities. Filtered and selected 8 non-homologous proteins were further employed in the pipeline to recognize non-homologous proteins against human anti-targets. Proteins that contain harmful effects due to the impact of a drug named anti-targets [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To screen out the significant similar sequence with familiar human anti-targets, PBIT database uses BLAST algorithm where they utilize those human anti-targets proteins based on different literature [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Again E-value\u0026thinsp;\u0026gt;\u0026thinsp;0.005 and % sequence identity\u0026thinsp;\u0026lt;\u0026thinsp;50 was set and all non-homologous sequences were selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Non-homology analysis against human gut flora proteomes\u003c/h2\u003e \u003cp\u003ePBIT also analysis human gut flora proteomes that make it easier to find out those highly similar sequences with human gut microbiota. It is known that gut microbiota plays an important role in human health that includes immune, metabolic and neurobehavioral characters [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. That\u0026rsquo;s why it is necessary to design such drugs whose target is non-homologous protein sequence of the gut microbiome. As result, such drugs could not be able to kill or hamper essential microbes found in human gut. For this, Pipeline builder for identification of target (PBIT) server was again used to identify non-homologous proteins against gut microbiota proteomes. As the third step of the pipeline builder, selected proteins were employed where E-value\u0026thinsp;\u0026gt;\u0026thinsp;0.001and % sequence identity\u0026thinsp;\u0026lt;\u0026thinsp;50 was set. Now non-homologous proteins were selected for the next investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Analysis of virulence factor\u003c/h2\u003e \u003cp\u003eUnderstanding the pathogenesis mechanism through the analysis of virulence factors can be a key to the discovery of new promising therapeutic targets [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, we have used VICMpred [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], VirulentPred [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and MP3 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] for the identification of the virulence property of the 9 EHPs. We have collected the results predicted combinedly by 2 out of the 3 tools. All the results were collected by using the provided default options by the servers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Druggability analysis and New Target Identification\u003c/h2\u003e \u003cp\u003eIdentification of a new drug target can be a new window for the discovery and development of a new drug against infectious or serious disease [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Druggability analysis is the examination of a protein that has the possible capability or binding affinity towards a drug or drug-like molecules. This Druggability analysis can introduce a new drug target against a drug. Here we used DrugBank, a comprehensive, online database that contains information on drugs and drug targets [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Target identification segment was utilized for this purpose and amino acid sequences in FASTA Format was the searching index. All other BLAST Parameters and Filters were set as default where the Expectation value was set 0.00001.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Segment IV: Structure Prediction and Structure Validation\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Secondary Structure Analysis\u003c/h2\u003e \u003cp\u003eThe interactions between neighboring polypeptides mainly design a protein\u0026rsquo;s secondary structure. When the elements of the secondary structure have folded together among each other, the 3D structure of the protein is formed. The databases namely SOPMA [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and PSIPRED [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] provide the secondary structure of a protein. These databases were used to predict the structure where protein sequence in FASTA format was the searching index for the websites and the rest of the parameters were set as default.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Essential hypothetical proteins 3D structure modelling\u003c/h2\u003e \u003cp\u003eThe protein 3D structure was determined based on two methods: template-based homology modelling, and trRosetta methods. The three-dimensional structure of the targeted protein was generated using the SWISS-MODEL server, which uses template search and then aligns the target sequence with the template structure to create the homology model [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. To construct the model with an accuracy equal to low-resolution x-ray crystallography, we only consider templates with \u0026ge;\u0026thinsp;30% sequence identity. Then the server, Robetta (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://robetta.bakerlab.org/\u003c/span\u003e\u003cspan address=\"https://robetta.bakerlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was employed to predict the 3D model by using trRosetta algorithm. It is a deep learning method based on direct energy minimizations that is the most accurate process of structure building provided by this server [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Finally, the built structure was optimized using Galaxy Refiner, with the best-refined model based on the lowest MolProbity and highest GDT-HA value [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Consequently, PyMOL 2.0 visualization software is used to visualize all of the refined structure files, which are in .pdb format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Protein structure validation assessment:\u003c/h2\u003e \u003cp\u003eThe reliability of a predicted 3D structure of a protein can be assessed by using various quality assessment tools. Here, in this study, we used SAVES version 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e which is a meta-server that runs six programs at once to check and validate protein structure during and after model refinement. This server validates the stereochemical consistency of a protein structure by performing residue by residue geometry and overall structure geometry. Furthermore, it also compares the results to good structures to see if an atomic model (3D) is compatible with its own amino acid sequence (1D) by assigning a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar, etc.). We run ERRAT [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], VARIFY 3D [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], PROVE [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], WHATCHECK [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], PROCHECK [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and Ramachandran Plot [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] from SAVES v6.0 to determine the consistency of the construct model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Performance assessment of the Study\u003c/h2\u003e \u003cp\u003eIn our study, we have applied the receiver operating characteristic (ROC) analysis for validating the accuracy of our bioinformatics tools used for the functional annotation of EHPs from \u003cem\u003eP. aeruginosa\u003c/em\u003e [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. We have collected 100 arbitrary protein functions of \u003cem\u003eP. aeruginosa\u003c/em\u003e along with their gene names using the same pipeline used prior to our study in the \u003cb\u003eSupplementary excel file\u003c/b\u003e. Two integer values namely \u0026ldquo;1\u0026rdquo; as a truly positive and \u0026ldquo;0\u0026rdquo; as a truly negative were assigned to classify the prediction. The confidence rating was denoted by \u0026ldquo;2\u0026rdquo;, \u0026ldquo;3\u0026rdquo;, \u0026ldquo;4\u0026rdquo; and \u0026ldquo;5\u0026rdquo; respectively. The higher number denotes greater level of confidence. The input file consists of 2 columns where 1st column contains binary numbers like 1 (True positive) and 0 (True negative) and the 2nd column contains a rate of confidence ranging from 2 to 5. For the present study, six levels were considered for determining the diagnostic efficacy. The ROC analysis was used for 12 individual functional annotation tools. The data were submitted to an online-based ROC curve generating web server in format-1 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The output result includes accuracy, sensitivity, specificity and the ROC area (\u003cb\u003eSupplementary File 1\u003c/b\u003e). The accuracy of our adopted pipeline is 97.42% which indicates a very high and reliable result for the bioinformatics tools that we used in our study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Functional annotation and domain analysis of EHPs\u003c/h2\u003e \u003cp\u003eThe functional annotation of the 18 EHPs was examined using 12 reliable platforms that predict protein superfamily, family, conserved domains, and Gene Ontology terms (GO). Here, the functional annotation was assigned with high confidence as we considered only that function that was similar in three or more programs. Consequently, the functional characterization categorizes these proteins into 9 functional categories, namely enzymes (deaminases, dehydrogenases, helicases, transferases, DNases, oxidoreductases, kinases, etc), transporter protein, bacterial outer membrane protein, folate binding protein, peptidase inhibitor protein, electron transporter protein, chromosome partition protein, ribosome maturation protein, pathogenesis-related protein. Nine of the 18 EHPs are enzymes (NP_252456.1, NP_252782.1, NP_253095.1, NP_253326.1, NP_250846.1, NP_252375.1, NP_253678.1, NP_253679.1, NP_253685.1), two are transporter proteins (NP_253252.1, NP_251676.1), and the remaining seven proteins are in the seven groups. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e enlists the 18 EHPs superfamily, functional family, molecular functions, biological functions, as well as their GO IDs and database IDs. Among these proteins, NP_249450.1 is a member of the folate-binding superfamily, with the aminomethyl transferase folate-binding domain as its functional family. Aminomethyl transferase and transaminase activity are the two molecular functions of this protein. Another protein sequence of NP_251676.1 was predicted belonging to the functional family that represents the periplasmic core domain found in a variety of ABC transporters. ATP binding, ATPase-coupled xenobiotic transmembrane transporter activity, efflux transmembrane transporter activity, and ATPase activity are some of the molecular functions of this protein. According to the GO annotation, there were 65 GO terminologies in total for the molecular function and biological process. These GO IDs can be used to retrieve Gene Ontology analysis of these 18 EHPs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional annotations of 18 essential hypothetical proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSerial\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNo.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eRefSeq\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSuperfamily\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eFamily\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGene ontology\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGo ID/Database integration\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBiological process\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMolecular function\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252456.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCytidine deaminase-\u003c/p\u003e \u003cp\u003elike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeoxycytidylate deaminase-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. tRNA wobble adenosine to inosine editing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.Hydrolase activity\u003c/p\u003e \u003cp\u003e2.Zinc ion binding\u003c/p\u003e \u003cp\u003e3.Catalytic activity\u003c/p\u003e \u003cp\u003e4.tRNA-specific adenosine\u003c/p\u003e \u003cp\u003e34-deaminase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0002100)\u003c/p\u003e \u003cp\u003e(GO:0016787)\u003c/p\u003e \u003cp\u003e(GO:0008270)\u003c/p\u003e \u003cp\u003e(GO:0003824)\u003c/p\u003e \u003cp\u003e(GO:0052717)\u003c/p\u003e \u003cp\u003eUniprot (W1MGT3)\u003c/p\u003e \u003cp\u003eInterpro (W1MGT3)\u003c/p\u003e \u003cp\u003eInterpro (IPR016192)\u003c/p\u003e \u003cp\u003eInterpro (IPR002125)\u003c/p\u003e \u003cp\u003eInterpro (IPR016193)\u003c/p\u003e \u003cp\u003eInterpro (IPR028883)\u003c/p\u003e \u003cp\u003ePfam (PF14437)\u003c/p\u003e \u003cp\u003eNCBI (532131853)\u003c/p\u003e \u003cp\u003eEMBL (ATNK01000135)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252782.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHotdog Thioesterase / thiol ester dehydratase-isomerase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThioesterase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Histidine biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Histidinol dehydrogenase activity\u003c/p\u003e \u003cp\u003e2. Zinc ion binding\u003c/p\u003e \u003cp\u003e3. NAD binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0000105) (GO:0004399)\u003c/p\u003e \u003cp\u003e(GO:0008270)\u003c/p\u003e \u003cp\u003e(GO:0051287) Uniprot (A0A448BY09)\u003c/p\u003e \u003cp\u003eInterpro(A0A448BY09)\u003c/p\u003e \u003cp\u003eInterpro(IPR029069)\u003c/p\u003e \u003cp\u003eInterpro (IPR006683)\u003c/p\u003e \u003cp\u003ePfam (PF03061)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253095.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncharacterized protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDna[CI] antecedent, DciA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Protein dephosphorylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Zinc ion binding\u003c/p\u003e \u003cp\u003e2. Protein tyrosine/serine/threonine phosphatase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0006470)\u003c/p\u003e \u003cp\u003e(GO:0008270)\u003c/p\u003e \u003cp\u003e(GO:0008138) Uniprot(Q9HW03)\u003c/p\u003e \u003cp\u003eInterpro (Q9HW03)\u003c/p\u003e \u003cp\u003eKEGG(pae:PA4405)\u003c/p\u003e \u003cp\u003eKEGG GM(pae:PA4405)\u003c/p\u003e \u003cp\u003eInterpro (IPR007922)\u003c/p\u003e \u003cp\u003ePfam (PF05258)\u003c/p\u003e \u003cp\u003eNCBI(489212117)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253252.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMATE_like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipid II flippaseMurJ, Polysaccharide biosynthesis C-terminal domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e Cell wall organization\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e Peptidoglycan biosynthetic process\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.\u003c/b\u003e Regulation of cell shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Lipid-linked peptidoglycan transporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0071555)\u003c/p\u003e \u003cp\u003e(GO:0009252)\u003c/p\u003e \u003cp\u003e(GO:0008360)\u003c/p\u003e \u003cp\u003e(GO:0015648)\u003c/p\u003e \u003cp\u003eUniprot (W1MQM4)\u003c/p\u003e \u003cp\u003eInterpro (W1MQM4)\u003c/p\u003e \u003cp\u003eInterpro (IPR004268)\u003c/p\u003e \u003cp\u003ePfam (PF03023)\u003c/p\u003e \u003cp\u003eNCBI (532135099)\u003c/p\u003e \u003cp\u003eEMBL (ATNK01000069)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253326.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycerol-3-phosphate (1)-acyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycerol-3-phosphate (1)-acyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e D-galacturonate catabolic process\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e D-glucuronate catabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Transferase activity, transferring acyl groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUniprot (Q9HVF5)\u003c/p\u003e \u003cp\u003eInterpro (Q9HVF5)\u003c/p\u003e \u003cp\u003eKEGG (pae: PA4636)\u003c/p\u003e \u003cp\u003eKEGG GM(pae:PA4636)\u003c/p\u003e \u003cp\u003eInterpro (IPR002123)\u003c/p\u003e \u003cp\u003ePfam (PF01553)\u003c/p\u003e \u003cp\u003eNCBI (489205664)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253368.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTonB-dependent receptor family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnergy transducer TonB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Viral process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. GTP binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0016032) (GO:0005525) Uniprot (Q9HVB6)\u003c/p\u003e \u003cp\u003eInterpro (Q9HVB6)\u003c/p\u003e \u003cp\u003eKEGG (pae:PA4679)\u003c/p\u003e \u003cp\u003eKEGG GM(pae:PA4679)\u003c/p\u003e \u003cp\u003eNCBI (489212281)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_249450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFolate-binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAminomethyl transferase folate-binding domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Iron-sulfur cluster assembly\u003c/p\u003e \u003cp\u003e2. Glycine decarboxylation via glycine cleavage system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Aminomethyl transferase activity\u003c/p\u003e \u003cp\u003e2. Transaminase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0019464) (GO:0004047)\u003c/p\u003e \u003cp\u003e(GO:0008483) Superfamily\u003c/p\u003e \u003cp\u003e(GO:0016226)\u003c/p\u003e \u003cp\u003eUniprot (Q9I5H0)\u003c/p\u003e \u003cp\u003eInterpro (Q9I5H0)\u003c/p\u003e \u003cp\u003eKEGG (pae:PA0759)\u003c/p\u003e \u003cp\u003eKEGG GM(pae:PA0759)\u003c/p\u003e \u003cp\u003eInterpro (IPR029043)\u003c/p\u003e \u003cp\u003eInterpro (IPR017703)\u003c/p\u003e \u003cp\u003eNCBI (489205124)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_250659.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibitor_I78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeptidase inhibitor I78 family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Cell adhesion\u003c/p\u003e \u003cp\u003e2. Homophilic cell adhesion via plasma membrane adhesion molecules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Calcium ion binding\u003c/p\u003e \u003cp\u003e2. Serine-type endopeptidase inhibitor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0007155)\u003c/p\u003e \u003cp\u003e(GO:0007156)\u003c/p\u003e \u003cp\u003e(GO:0005509)\u003c/p\u003e \u003cp\u003e(GO:0004867)\u003c/p\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003eUniprot (Q9I2D5)\u003c/p\u003e \u003cp\u003eInterpro (Q9I2D5)\u003c/p\u003e \u003cp\u003eKEGG (pae:PA1969)\u003c/p\u003e \u003cp\u003eKEGG GM (pae:PA1969)\u003c/p\u003e \u003cp\u003eInterpro (IPR021719)\u003c/p\u003e \u003cp\u003ePfam (PF11720)\u003c/p\u003e \u003cp\u003eNCBI (489210309)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_250846.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNase I-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEndonuclease/Exonuclease/phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e Endonuclease activity\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e Exonuclease activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0004519)\u003c/p\u003e \u003cp\u003e(GO:0004527)\u003c/p\u003e \u003cp\u003eUniprot (A0A6N0KLP9)\u003c/p\u003e \u003cp\u003eInterpro (A0A6N0KLP9)\u003c/p\u003e \u003cp\u003eInterpro (IPR036691)\u003c/p\u003e \u003cp\u003eInterpro (IPR005135)\u003c/p\u003e \u003cp\u003ePfam (PF03372)\u003c/p\u003e \u003cp\u003eEMBL (CP054572)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_251676.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLolE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacB-like periplasmic core domain, Lipoprotein-releasing ABC transporter permease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e Lipoprotein localization to outer membrane\u003c/p\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e Lipoprotein transport\u003c/p\u003e \u003cp\u003e\u003cb\u003e3.\u003c/b\u003e Protein localization to outer membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. ATP binding\u003c/p\u003e \u003cp\u003e2. ATPase-coupled xenobiotic transmembrane transporter activity\u003c/p\u003e \u003cp\u003e3. Efflux transmembrane transporter activity\u003c/p\u003e \u003cp\u003e4. ATPase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0044874)\u003c/p\u003e \u003cp\u003e(GO:0042953)\u003c/p\u003e \u003cp\u003e(GO:0089705)\u003c/p\u003e \u003cp\u003e(GO:0005524)\u003c/p\u003e \u003cp\u003e(GO:0008559)\u003c/p\u003e \u003cp\u003e(GO:0015562)\u003c/p\u003e \u003cp\u003e(GO:0016887)\u003c/p\u003e \u003cp\u003eUniprot (Q9HZL8)\u003c/p\u003e \u003cp\u003eInterpro (Q9HZL8)\u003c/p\u003e \u003cp\u003eKEGG (pae:PA2986)\u003c/p\u003e \u003cp\u003eKEGG GM (pae:PA2986)\u003c/p\u003e \u003cp\u003eInterpro (IPR003838)\u003c/p\u003e \u003cp\u003eInterpro (IPR011925)\u003c/p\u003e \u003cp\u003eInterpro (IPR025857)\u003c/p\u003e \u003cp\u003ePfam (PF02687)\u003c/p\u003e \u003cp\u003ePfam (PF12704)\u003c/p\u003e \u003cp\u003eNCBI (489210993)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252171.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe-S cluster assembly (FSCA) domain-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIron-sulfur cluster assembly protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Iron-sulfur cluster assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.ATPase activity\u003c/p\u003e \u003cp\u003e2.ATP binding\u003c/p\u003e \u003cp\u003e3.Iron-sulfur cluster binding\u003c/p\u003e \u003cp\u003e4.Metal ion binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0016226)\u003c/p\u003e \u003cp\u003e(GO:0016887)\u003c/p\u003e \u003cp\u003e(GO:0005524)\u003c/p\u003e \u003cp\u003e(GO:0051536)\u003c/p\u003e \u003cp\u003e(GO:0046872)\u003c/p\u003e \u003cp\u003eUniprot (A0A3S4MTX6)\u003c/p\u003e \u003cp\u003eInterpro(A0A3S4MTX6)\u003c/p\u003e \u003cp\u003eInterpro (IPR034904)\u003c/p\u003e \u003cp\u003eInterpro (IPR002744)\u003c/p\u003e \u003cp\u003eInterpro (IPR019591)\u003c/p\u003e \u003cp\u003eInterpro (IPR000808)\u003c/p\u003e \u003cp\u003eInterpro (IPR027417)\u003c/p\u003e \u003cp\u003eInterpro (IPR033756)\u003c/p\u003e \u003cp\u003ePfam (PF01883)\u003c/p\u003e \u003cp\u003ePfam (PF10609)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252375.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbam_trans_N (Carbamoyltransferase N-terminus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA N6-adenosine threonyl carbamoyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. tRNA threonyl carbamoyl adenosine modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Metalloendopeptidase activity\u003c/p\u003e \u003cp\u003e2. Iron ion binding\u003c/p\u003e \u003cp\u003e3. N(6)-L-threonyl carbamoyl adenine synthase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0002949) (GO:0004222)\u003c/p\u003e \u003cp\u003e(GO:0005506)\u003c/p\u003e \u003cp\u003e(GO:0061711)\u003c/p\u003e \u003cp\u003eUniprot (Q9HXV5)\u003c/p\u003e \u003cp\u003eInterpro (Q9HXV5)\u003c/p\u003e \u003cp\u003eKEGG (pae:PA3685)\u003c/p\u003e \u003cp\u003eKEGG GM (pae:PA3685)\u003c/p\u003e \u003cp\u003eInterpro (IPR043129)\u003c/p\u003e \u003cp\u003eInterpro (IPR000905)\u003c/p\u003e \u003cp\u003eInterpro (IPR022496)\u003c/p\u003e \u003cp\u003ePfam (PF00814)\u003c/p\u003e \u003cp\u003eNCBI (887492937)\u003c/p\u003e \u003cp\u003eEMBL (AE004091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253374.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMukE (MukE is part of the MukBEF condensin complex)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBacterial condensin subunit MukE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Cell cycle\u003c/p\u003e \u003cp\u003e2. Cell division\u003c/p\u003e \u003cp\u003e3. DNA replication\u003c/p\u003e \u003cp\u003e4. Chromosome segregation\u003c/p\u003e \u003cp\u003e5. Chromosome condensation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. GTP binding\u003c/p\u003e \u003cp\u003e2. GTPase activity\u003c/p\u003e \u003cp\u003e3. Translation elongation factor activity\u003c/p\u003e \u003cp\u003e4. ATP binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0007049)\u003c/p\u003e \u003cp\u003e(GO:0051301)\u003c/p\u003e \u003cp\u003e(GO:0006260)\u003c/p\u003e \u003cp\u003e(GO:0007059)\u003c/p\u003e \u003cp\u003e(GO:0030261)\u003c/p\u003e \u003cp\u003e(GO:0005525)\u003c/p\u003e \u003cp\u003e(GO:0003924)\u003c/p\u003e \u003cp\u003e(GO:0003746)\u003c/p\u003e \u003cp\u003e(GO:0005524)\u003c/p\u003e \u003cp\u003eUniprot (A0A448BSU5)\u003c/p\u003e \u003cp\u003eInterpro (A0A448BSU5)\u003c/p\u003e \u003cp\u003eInterpro (IPR042038)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253434.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.RimP N-terminal domain\u003c/p\u003e \u003cp\u003e2.RimP C-terminal SH3 domain\u003c/p\u003e \u003cp\u003e(also known as yhbC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRimP N-terminal domain, RimP C-terminal SH3 domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.Ribosomal small subunit biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0042274)\u003c/p\u003e \u003cp\u003eUniprot (A0A3S4MTG9)\u003c/p\u003e \u003cp\u003eInterpro (A0A3S4MTG9)\u003c/p\u003e \u003cp\u003eInterpro (IPR003728)\u003c/p\u003e \u003cp\u003eInterpro (IPR028998)\u003c/p\u003e \u003cp\u003eInterpro (IPR036847)\u003c/p\u003e \u003cp\u003eInterpro (IPR028989)\u003c/p\u003e \u003cp\u003eInterpro (IPR035956)\u003c/p\u003e \u003cp\u003ePfam (PF02576)\u003c/p\u003e \u003cp\u003ePfam (PF17384)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253455.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBet v1-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyketide cyclase /dehydrase and lipid transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Ubiquinone biosynthetic process\u003c/p\u003e \u003cp\u003e2. Cellular respiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. Ubiquinone binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(GO:0006744)\u003c/p\u003e \u003cp\u003e(GO:0045333)\u003c/p\u003e \u003cp\u003e(GO:0048039)\u003c/p\u003e \u003cp\u003eSUPERFAMILY 1.75\u003c/p\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003eIPR005031\u003c/p\u003e \u003cp\u003eUniprot (A0A448BT54)\u003c/p\u003e \u003cp\u003eInterpro (A0A448BT54)\u003c/p\u003e \u003cp\u003eInterpro (IPR005031)\u003c/p\u003e \u003cp\u003eInterpro (IPR023393)\u003c/p\u003e \u003cp\u003ePfam (PF03364)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253678.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAD/NAD(P)-binding domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAD dependent oxidoreductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.Oxidation-reduction process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.Oxidoreductase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0055114)\u003c/p\u003e \u003cp\u003e(GO:0016491)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253679.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAD(P)-linked oxidoreductase/\u003c/p\u003e \u003cp\u003eAldo-keto reductase (AKR) superfamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAldo/keto reductase family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Daunorubicin metabolic process\u003c/p\u003e \u003cp\u003e2. Doxorubicin metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.Oxidoreductase activity\u003c/p\u003e \u003cp\u003e2.D-threo-aldose 1-dehydrogenase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003cp\u003e(GO:0044597)\u003c/p\u003e \u003cp\u003e(GO:0044598)\u003c/p\u003e \u003cp\u003e(GO:0047834)\u003c/p\u003e \u003cp\u003eUniprot (A0A3S4Q0Y1)\u003c/p\u003e \u003cp\u003eInterpro (A0A3S4Q0Y1)\u003c/p\u003e \u003cp\u003eInterpro(IPR023210)\u003c/p\u003e \u003cp\u003eInterpro (IPR036812)\u003c/p\u003e \u003cp\u003ePfam (PF00248)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253685.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein kinase-like (PK-like)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhosphotransferase enzyme family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. Protein phosphorylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1. ATP binding\u003c/p\u003e \u003cp\u003e2. Protein serine/threonine kinase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePfam\u003c/p\u003e \u003cp\u003e(GO:0006468)\u003c/p\u003e \u003cp\u003e(GO:0005524)\u003c/p\u003e \u003cp\u003e(GO:0004674)\u003c/p\u003e \u003cp\u003eUniprot (A0A3S5E573)\u003c/p\u003e \u003cp\u003eInterpro (A0A3S5E573)\u003c/p\u003e \u003cp\u003eInterpro (IPR011009)\u003c/p\u003e \u003cp\u003eEMBL (LR134300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Subcellular Localizations of EHPs\u003c/h2\u003e \u003cp\u003eTo identify the cellular localization of our 18 EHPs, the websites PSORTb and CELLO were utilized. According to the data of PSORTb, among 18 essential hypothetical proteins, 6 proteins belong to cytoplasmic protein, 8 proteins belong to the location of the cytoplasmic membrane and the remaining 4 proteins are considered as unknown. The database, CELLO depicted that 14 proteins are cytoplasmic protein, 2 proteins are considered as inner membrane protein and the rest 2 are periplasmic protein. This is the generalized concept of the cellular location which is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand supplementary table 1\u003c/b\u003e. The existence of the transmembrane helix was also figured out and this can help to carry out the function of a protein through transmembrane transportation. The amount of transmembrane helix was given in \u003cb\u003esupplementary table 1\u003c/b\u003e. The presence of signal peptide was also investigated from the three websites SignalP 4.1, PROTTER and PrediSi. Among 18 proteins 14 proteins (NP_252456.1, NP_252782.1, NP_253095.1, NP_253326.1, NP_253368.1, NP_249450.1, NP_250846.1, NP_252171.1, NP_252375.1, NP_253374.1, NP_253455.1, NP_253678.1, NP_253679.1 and NP_253685.1) do not contain any signal peptide and one protein contain signal peptide unanimously. Whereas the remaining proteins (NP_253252.1, NP_251676.1 and NP_253434.1) are containing signal peptides from any of a website (\u003cb\u003esupplementary table 1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Physicochemical properties Analysis\u003c/h2\u003e \u003cp\u003eWe have searched for the physicochemical properties of 18 EHPs which is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All the proteins had molecular weight ranging from 13335.11 to 56122.54. The highest molecular weight was observed to be 56122.54 for the NP_253252.1 protein, a probable lipid II flippaseMurJ [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The theoretical pI (Isoelectric Point) indicates the pH at which the charge of an amino acid of a protein remains neutral. Therefore, no movement occurs when placed in an electric field with a direct current. This parameter comes in handy as proteins are dense and stable at an isoelectric pH [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The theoretical pI ranged from 4.52 to 10.71. Both of these parameters (molecular weight and theoretical pI) help visualize the Two-dimensional gel electrophoresis or (2-DE). Hence contributes to the scientific examinations of these hypothetical proteins [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The aliphatic index can be an effective indicator for determining the thermostability of some protein molecules [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. A protein molecule with a higher aliphatic index indicates its higher range of temperature at which it gains its thermostability [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The aliphatic index tabulated for our protein group ranged from 83.13 to 133.96. The NP_253252.1 protein showed the maximum thermostability and NP_252456.1 with the lowest. The parameter called Instability index determines a protein whether it\u0026rsquo;s stable or unstable in a test tube [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. For our analysis, we set the cutoff value to 40 where the value below 40 indicates a protein to be stable and above 40 predicts it as an unstable protein. Total 9 proteins (NP_252456.1, NP_252782.1, NP_253252.1, NP_253326.1, NP_249450.1, NP_251676.1, NP_252171.1, NP_252375.1, NP_253679.1) out of 18 proteins of interest found to be stable with Instability index values of 25.65, 37.46, 36.08, 38.61, 31.65, 38.17, 32.05, 29.03, 32.97 respectively. The grand average of hydropathy (GRAVY) determines the extent of protein-water interaction which is calculated by dividing the aggregate of all the amino acids hydropathy values with the total number of residues in the given sequence [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. the GRAVY values lied between \u0026minus;\u0026thinsp;0.427 to 0.857. The lower the GRAVY value, the more a protein interacts with water [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The NP_253095.1 protein was found to be most interactive among all these proteins having a GRAVY value of -0.427.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical properties of 18 essential hypothetical proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefSeq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolecular weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical pI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal number of negatively charged residues (Asp\u0026thinsp;+\u0026thinsp;Glu)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal number of positively charged residues (Arg\u0026thinsp;+\u0026thinsp;Lys)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInstability index (II)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAliphatic index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGrand average of hydropathicity (GRAVY)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252456.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19937.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e869\u003c/sub\u003eH\u003csub\u003e1409\u003c/sub\u003eN\u003csub\u003e265\u003c/sub\u003eO\u003csub\u003e255\u003c/sub\u003eS\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.65\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e83.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252782.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14871.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e658\u003c/sub\u003eH\u003csub\u003e1078\u003c/sub\u003eN\u003csub\u003e188\u003c/sub\u003eO\u003csub\u003e193\u003c/sub\u003eS\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.46\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253095.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15057.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e657\u003c/sub\u003eH\u003csub\u003e1080\u003c/sub\u003eN\u003csub\u003e210\u003c/sub\u003eO\u003csub\u003e188\u003c/sub\u003eS\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.65\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253252.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56122.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e2643\u003c/sub\u003eH\u003csub\u003e4201\u003c/sub\u003eN\u003csub\u003e651\u003c/sub\u003eO\u003csub\u003e651\u003c/sub\u003eS\u003csub\u003e19\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.08\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e133.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253326.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43779.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1955\u003c/sub\u003eH\u003csub\u003e3058\u003c/sub\u003eN\u003csub\u003e554\u003c/sub\u003eO\u003csub\u003e569\u003c/sub\u003eS\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.61\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e87.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253368.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24873.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1111\u003c/sub\u003eH\u003csub\u003e1779\u003c/sub\u003eN\u003csub\u003e317\u003c/sub\u003eO\u003csub\u003e319\u003c/sub\u003eS\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.37\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_249450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33667.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1492\u003c/sub\u003eH\u003csub\u003e2415\u003c/sub\u003eN\u003csub\u003e425\u003c/sub\u003eO\u003csub\u003e446\u003c/sub\u003eS\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.65\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_250659.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13335.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e567\u003c/sub\u003eH\u003csub\u003e936\u003c/sub\u003eN\u003csub\u003e178\u003c/sub\u003eO\u003csub\u003e181\u003c/sub\u003eS\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.34\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e83.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_250846.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27693.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1245\u003c/sub\u003eH\u003csub\u003e1962\u003c/sub\u003eN\u003csub\u003e380\u003c/sub\u003eO\u003csub\u003e330\u003c/sub\u003eS\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.29\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_251676.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47387.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e2139\u003c/sub\u003eH\u003csub\u003e3484\u003c/sub\u003eN\u003csub\u003e582\u003c/sub\u003eO\u003csub\u003e587\u003c/sub\u003eS\u003csub\u003e20\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.17\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252171.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38888.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1711\u003c/sub\u003eH\u003csub\u003e2780\u003c/sub\u003eN\u003csub\u003e482\u003c/sub\u003eO\u003csub\u003e517\u003c/sub\u003eS\u003csub\u003e16\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.05\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e102.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252375.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24180.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1081\u003c/sub\u003eH\u003csub\u003e1707\u003c/sub\u003eN\u003csub\u003e303\u003c/sub\u003eO\u003csub\u003e313\u003c/sub\u003eS\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.03\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e102.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253374.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26354.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1166\u003c/sub\u003eH\u003csub\u003e1811\u003c/sub\u003eN\u003csub\u003e315\u003c/sub\u003eO\u003csub\u003e366\u003c/sub\u003eS\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.53\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253434.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17171.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e763\u003c/sub\u003eH\u003csub\u003e1208\u003c/sub\u003eN\u003csub\u003e206\u003c/sub\u003eO\u003csub\u003e236\u003c/sub\u003eS\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.54\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e105.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253455.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16000.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e720\u003c/sub\u003eH\u003csub\u003e1130\u003c/sub\u003eN\u003csub\u003e190\u003c/sub\u003eO\u003csub\u003e208\u003c/sub\u003eS\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.00\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253678.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42109.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1866\u003c/sub\u003eH\u003csub\u003e3011\u003c/sub\u003eN\u003csub\u003e551\u003c/sub\u003eO\u003csub\u003e541\u003c/sub\u003eS\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.61\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253679.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29030.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1281\u003c/sub\u003eH\u003csub\u003e2067\u003c/sub\u003eN\u003csub\u003e373\u003c/sub\u003eO\u003csub\u003e386\u003c/sub\u003eS\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.97\u003c/p\u003e \u003cp\u003e(Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e101.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253685.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24985.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e1112\u003c/sub\u003eH\u003csub\u003e1791\u003c/sub\u003eN\u003csub\u003e337\u003c/sub\u003eO\u003csub\u003e311\u003c/sub\u003eS\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.71\u003c/p\u003e \u003cp\u003e(Unstable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e104.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Protein-protein interaction network analysis\u003c/h2\u003e \u003cp\u003eThe PPI represents the connection among the 9 stable EHPs and their corresponding functionally relative proteins from \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01. The network has 261 nodes and 269 edges for 9 proteins of interest. Here, the network is provided with 11 subnetworks (Hubs) with a minimum of 3 nodes each. The nodes with only 3 connections (Degree) are considered as Islands (ostA and PA1847) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The node degree and Betweenness centrality range from 3 to 45 and 4750 to 17881.76, respectively. The interaction among the hub proteins can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The size and color gradient of the nodes determine the degree of a protein. A node degree reveals the extent of interaction of a particular node with other nodes. The nodes with lower degree values are colored green namely PA4992 (24), PA3481 (23), PA4093 (20), PA4636 (18). The color gradually turned into deep purple by the increase of node degree values. Nodes with enlarged size similarly denotes increased node degree values such as PA2986 (45), PA0759 (41), PA4562 (38), PA3685 (32), PA3767 (28) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nodes in cyan blue meaning 2 or more interactions with their corresponding subnetworks. Betweenness centrality is a topological measure that typically determines the number of shortest paths through nodes. The nodes with a higher degree and betweenness centrality values represent vital proteins for signal trafficking of the cellular system [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The function of all proteins in the network are collected from NCBI using their associated Entrez IDs and listed in the \u003cb\u003esupplementary table 2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of proteins with their Reference sequence, Uniprot ID, Protein name, Node degree value, and Betweenness centrality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef seq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniProt ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003cp\u003ecentrality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_251676.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HZL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA2986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9681.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_249450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9I5H0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA0759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17881.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253252.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HVM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA4562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9315.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252375.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HXV5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA3685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8742.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252456.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HXM8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA3767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11489.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253679.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HUH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA4992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10103.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252171.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HYC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA3481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5329.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252782.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HWT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA4093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4750.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253326.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9HVF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA4636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6466.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_249286.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9I5U2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eostA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10848.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_250538.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9I2P8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA1847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5698.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Non-homology analysis against human proteome, human anti-targets and human gut flora proteomes\u003c/h2\u003e \u003cp\u003eTo introduce a novel target for a drug it must be non-homologous against human proteome, human anti-targets, human gut flora proteomes. Utilizing pipeline builder from the Pipeline builder for identification of target (PBIT) server, 9 protein sequences were inputted to find out the highly similar sequence with human proteome. Among the 9 EHPs sequences, one sequence was homologous with the human proteome. Filtering that one sequence, 8 non-homologous proteins were selected for the next pipeline analysis to find out the non-homologous proteins against human anti-targets. Among that 8 entered sequences, significant similar sequences of human anti-target proteins were screen out. This result depicted that 7 proteins are non-homologous and one protein is homologous to the human anti-target where this one homologous protein was omitted from the study. After the filtration, selected 7 proteins were further inputted onto the pipeline builder to analyze non-homologous proteins against human gut flora proteomes. This time 2 proteins were screen out because of containing high sequence similarity with the proteomes of the beneficiary microbes belong to the human gut. Then finally the sequences of 5 non-homologous EHPs were selected for the next parameter of finding virulence capability. The details of the non-homology analysis are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAspects of the proteins like non-homology to human proteins and proteins of human gut flora, virulence of the pathogen, druggability for the 9 EHPs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSerial no\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProtein\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNon-homology analysis against human proteome\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNon-homology analysis against human anti-targets\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNon-homology analysis against gut microbiota proteomes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eVirulence analysis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eDruggability analysis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252456.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOld target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252782.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253252.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHomologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253326.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_249450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVirulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_251676.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVirulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252171.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHomologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_252375.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHomologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNovel target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNP_253679.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-homologous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon- virulent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOld target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Virulence factor\u003c/h2\u003e \u003cp\u003eThe virulent EHPs from \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 are enlisted in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. VICMpred is a Support Vector Machine (SVM) based webserver that predicted all of the 9 EHPs as non-virulent with 70.75% accuracy [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. VirulentPred is also based on bi-layer cascade SVM with five-fold increased cross-validation methods that give 81.8% prediction accuracy [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Total 3 proteins namely NP_249450.1 (e-106), NP_251676.1 (e-171), NP_253679.1 (7e-77) were predicted as virulent by VirulentPred in \u003cem\u003ep. aeruginosa\u003c/em\u003e PA01 strain utilizing the Similarity-Based search through PSI-BLAST. Another webserver called MP3 uses an integrated SVM-HMM approach which commonly predicted NP_251676.1 as a pathogenic protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.7 A Possible New Drug Target Identification\u003c/h2\u003e \u003cp\u003eAlong with the two virulent EHPs, other 7 sequences of EHPs were employed to the DrugBank server for the identification of potentially new drug candidates. This server showed that NP_252456.1 contains one drug target against the drug Imidazole (E value: 5.62144e-18; Bit score: 75.485; Query length: 182; Alignment length: 77) and two drug targets were exhibited by the protein NP_253679.1 against the drug Nicotinamide adenine dinucleotide phosphate (E value: 3.79487e-15; Bit score: 72.4034; Query length: 270; Alignment length: 213) and Nicotinamide adenine dinucleotide phosphate (E value: 1.77261e-14; Bit score: 70.8626; Query length: 270; Alignment length: 217). The remaining 7 proteins (NP_252782.1, NP_253252.1, NP_253326.1, NP_249450.1, NP_251676.1, NP_252171.1 and NP_252375.1) was considered as a fresh or new drug target by the DrugBank database. This website also revealed that our targeted two proteins named NP_249450.1 and NP_251676.1 displayed zero matches for the drug target which means they are new potential drug candidates with druggability. The overall results are in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Analyzing Secondary Structure\u003c/h2\u003e \u003cp\u003eBased on the findings of segment III, we selected two proteins for the next level investigations that match all the criteria of segment III. As they are hypothetical proteins, they must lack some information. For this, to suggest them as a new drug target we explored their secondary structure. SOPMA and PSIPRED were the web tools that were used for the secondary structure analysis. According to the SOPMA server, the secondary structure of NP_249450.1 had Alpha helix (Hh): 126 (40.13%); Extended strand (Ee): 57 (18.15%); Beta turn (Tt): 20 (6.37%), and Random coil (Cc): 111 (35.35%) where the parameters were set as Window width: 17; Similarity threshold: 8 and Number of states: 4. The protein, NP_251676.1 had Alpha helix (Hh): 206 (47.58%); Extended strand (Ee): 83(19.17%); Beta turn (Tt): 23(5.31%) and Random coil (Cc): 121 (27.94%) with the same parameter as before. The results from SOPMA database for both proteins are given in \u003cb\u003esupplementary table 3\u003c/b\u003e. The PSIPRED sequence plot and PSIPRED cartoon plot were provided as a result of the PSIPRED web servers. The sequence plot and cartoon plot structure described that goldenrod (semi-yellow) color is for the extracellular strand domain, pink color is for helix; grey color is for coil and blackish blue is for the confidence of the structure. According to this, NP_249450.1 showed more coil in its secondary structure whereas NP_251676.1 showed more helix. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e is the secondary structure of NP_249450.1 from PSIPRED and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e(b)\u003c/b\u003e is from SOPMA websites. Besides Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e is the secondary structure of NP_251676.1 from PSIPRED and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(b)\u003c/b\u003e is from SOPMA database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Essential hypothetical proteins 3D structure modelling\u003c/h2\u003e \u003cp\u003eOnly proteins that passed all of the above-mentioned pipeline analyses were assigned a three-dimensional structural conformation. Two proteins, NP_249450.1 and NP_251676.1, were subjected to a thorough pipeline review and thus have the potential to be used as new drug targets. As a result, these two proteins were subjected to 3D structural conformation determination using two methods: template-based homology modelling from SWISS-MODEL and \u003cem\u003eab-initio\u003c/em\u003e modeling using the trRosetta algorithm from the Robetta server. For template-based homology modelling, we searched for templates from SWISS-MODEL for these two proteins. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1vly.1\u003c/span\u003e and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6f3z.2\u003c/span\u003e were the best template for NP_249450.1 and NP_251676.1, respectively. The templates were chosen based on several parameters, including the Global Model Quality Estimation (GMQE), Qualitative Model Energy ANalysis (QMEAN), Z-score, sequence identity, sequence similarity, sequence coverage, oligo-state of the chosen templates, and so on. The template \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1vly.1\u003c/span\u003e was actually a 1.30 \u0026Aring; resolution x-ray diffraction crystallography structure of a putative aminomethyltransferase (ygfz) from \u003cem\u003eE. coli\u003c/em\u003e. This template shared 30.23% sequence identity with the 314 aa long NP_249450.1 protein, which spans from (4-307) aa. The template \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6f3z.2\u003c/span\u003e, on the other hand, was a complex of \u003cem\u003eE. coli\u003c/em\u003e LolA and the periplasmic domain of LolC that was also identified by x-ray diffraction crystallography at a resolution of 2.00 \u0026Aring;. The sequence identity was 30.73%, spanning (67\u0026ndash;290) amino acids out of the 433 amino acids in the NP_251676.1 protein. Both of these templates had a monomer oligo-state. Finally, using \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1vly.1\u003c/span\u003e and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6f3z.2\u003c/span\u003e templates, the structures of NP_249450.1 and NP_251676.1 EHPs were formed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec. Structure prediction by Robetta server illustrated that the provided model was build using trRefineRosetta modelling (\u003cem\u003eab-initio\u003c/em\u003e modeling using the trRosettaalgorithm). Structure of NP_249450.1 showed 0.79 score as confidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) while NP_251676.1 showed 0.81 score as confidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Consequently, the structures from SWISS-MODEL were then refined from Galaxy Refiner where model 2 for NP_249450.1 and Model 5 for NP_251676.1 were downloaded after final refinement. For the NP_249450.1 and NP_251676.1 proteins, the lowest MolProbity was 1.738 (Model 2) and 1.729 (Model 5), respectively, while the initial score was 2.280 and 2.299. Also, the structures from Robbetta were refined from Galaxy Refiner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Protein structure validation assessment\u003c/h2\u003e \u003cp\u003eThe predicted protein structure was validated by SAVES v6.0 server which runs six programs simultaneously to evaluate the quality of the build model. The ERRAT value served as the model's overall quality element. The overall quality factor for the NP_249450.1 protein structure from SWISS-MODEL and Robetta, respectively, was 87.6325% and 92.459%. It was 93.3649% and 98.063% for NP_251676.1 from these two servers, respectively. In \u003cb\u003esupplementary Fig.\u0026nbsp;1\u003c/b\u003e, bar plots depict the overall quality factor from ERRAT. VARIFY3D conducts an analysis in which a structure passes if at least 80% of the amino acids in the 3D/1D profile have a score of \u0026gt;\u0026thinsp;=\u0026thinsp;0.2. Three of the four structures passed this parameter (two from SWISS-MODEL and one from Robetta), while one structure failed for NP_251676.1 from Robetta. WHATCHECK included a color box with a number within it that reflects 46 different criteria, with the green, yellow, and maroon colors representing OK, warning, and error, respectively. The overall summary report is OK for all four structures. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e included a comprehensive report on the consistency of the four structures that we retrieved from the SAVES v6.0 server. On the contrary, structures from SWISS-MODEL failed to pass the PROVE parameters, while structures from Robetta were placed in warning categories due to atomicB-factors, and the protein atoms having absolute Z-scores\u0026thinsp;\u0026gt;\u0026thinsp;3. Ramachandran plot analysis from the PROCHECK program also demonstrated that more than 94% of residues were in the most favored region for all four structures from both SWISS-MODEL and Robetta. It was 97.3% for NP_251676.1 protein from Robetta, with 0.0% residues in the disallowed region. Ramachandran plot analysis unveiled that the generated structures of the proteins represent an excellent degree of validity and reliability, which is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eTable 6:\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eThree-dimensional structure validation of the predicted two hypothetical proteins from SAVES v6.0 server\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:670.25pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:92.45pt;border:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:right;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eSaves\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:right;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eResult\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eProtein Name\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:66.2pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eERRAT\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:76.2pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eVARIFY 3D\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.05pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003ePROVE\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.85pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eWHATCHECK\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:102.5pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003ePROCHECK\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.0pt;border:solid windowtext 1.0pt;border-left: none;padding:0in 5.4pt 0in 5.4pt;height:75.55pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:19px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eRamachandran\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style='font-size:19px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003ePlot\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cem\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e(% residue in the most favored region)\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:92.45pt;border:solid windowtext 1.0pt;border-top: none;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eNP_249450.1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003e(Swiss Model)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:66.2pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOverall Quality Factor\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e87.6325\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:76.2pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e97.70% of the residues have\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eaveraged 3D-1D score \u0026gt;= 0.2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.05pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eBuried outlier protein atoms total\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efrom 1 Model: 6.1%\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efail\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.85pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:#FFFFAA;background:#990000;'\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e34567\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e891011\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e131415161718\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e19\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e20\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e212223\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e24\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e2627\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e28\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e29\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e303132\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e33\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e3435\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e36\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;border:none windowtext 1.0pt;padding:0in;background:#55FF55;\"\u003e37\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e38\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e39\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e40\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e414243\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e4445\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:102.5pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOut of 8 evaluations\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eErrors: 3\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eWarning: 2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass: 3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.0pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:107.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e94.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:92.45pt;border:solid windowtext 1.0pt;border-top: none;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eNP_249450.1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003e(Robetta)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:66.2pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOverall Quality Factor\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e92.459\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:76.2pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e94.90% of the residues have\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eaveraged 3D-1D score \u0026gt;= 0.2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.05pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eBuried outlier protein atoms total\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efrom 1 Model: 4.1%\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ewarning\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.85pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:#FFFFAA;background:#990000;'\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e345678\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e91011\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e131415161718\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e19\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e20\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e21222324\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e2627\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e28\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e29\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e303132\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e33\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e34\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e35363738\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e39\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e40\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e41\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e42\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e43\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e4445\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:102.5pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOut of 8 evaluations\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eErrors: 3\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eWarning: 2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass: 3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.0pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:103.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e94.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:92.45pt;border:solid windowtext 1.0pt;border-top: none;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eNP_251676.1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003e(Swiss Model)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:66.2pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOverall Quality Factor\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e93.3649\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:76.2pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e82.59% of the residues have\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eaveraged 3D-1D score \u0026gt;= 0.2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.05pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eBuried outlier protein atoms total\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efrom 1 Model: 5.4%\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efail\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.85pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:#CE0606;'\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e34567\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e8\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e9\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e1011\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e131415161718\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e19\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e20\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e2122232425\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e26\u003cspan style=\"border:none windowtext 1.0pt;padding:0in;\"\u003e27\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e28\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e29\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e303132\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e33\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e34\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e35363738\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e39\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e40\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e41\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e42\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e43\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e4445\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:102.5pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOut of 8 evaluations\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eErrors: 2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eWarning: 4\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass: 2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.0pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:105.25pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e95.3 %\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:92.45pt;border:solid windowtext 1.0pt;border-top: none;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003eNP_251676.1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Times New Roman\",serif;'\u003e(Robetta)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:66.2pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOverall Quality Factor\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e98.063\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:76.2pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e66.97% of the residues have\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eaveraged 3D-1D score \u0026gt;= 0.2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eFail\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.05pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eBuried outlier protein atoms total\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003efrom 1 Model: 4.2%\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ewarning\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.85pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:#CE0606;'\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e345678\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e91011\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e12\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e131415161718\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e19\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e20\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e2122232425\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e2627\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e28\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e29\u003c/span\u003e\u003cspan style=\"font-family: Helvetica;color:black;background:#55FF55;\"\u003e303132\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e33\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e3435\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e363738\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e39\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e40\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e41\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#FFFFAA;background:#990000;\"\u003e42\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:black;background:#55FF55;\"\u003e43\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;background:yellow;\"\u003e4445\u003c/span\u003e\u003cspan style=\"font-family:Helvetica;color:#000099;border:none windowtext 1.0pt;padding:0in;background:#55FF55;\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:102.5pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOut of 8 evaluations\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eErrors: 2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eWarning: 1\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ePass: 5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:117.0pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:98.5pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e97.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 is an omnipresent pathogenic bacterium that can cause acute and chronic infection to humans by contaminating environmental water and food, daily food spoilage, and infections. It is a rising concern for its increasing resistance against a broad range of antimicrobials. The biofilm-forming ability and evolution of antibiotic tolerance shapes \u003cem\u003epseudomonas\u003c/em\u003e isolate highly resistant against imipenem (95.3%), trimethoprim-sulfamethoxazole (69.8%), aztreonam (60.5%), chloramphenicol (45.3%), and meropenem (27.9%) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Factors like chromosomal mutations and transferring of resistant genes through horizontal gene transfer contribute to its broad-spectrum drug resistance property [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Thus, it is necessary to introduce a new drug target when there will be noticed multi-drug resistance for any diseases or problem. \u003cem\u003eIn silico\u003c/em\u003e process has a great advantage for the identification of new drug targets in that situation within a very short time. Consequently, to combat the ever-increasing danger of antibiotic resistance, identifying novel drug targets is a dire necessity. A drug target should have some properties before it is considered as a new target which includes being non-homologous to human proteome, human anti-targets, human gut microbiota, having virulence capability, having druggability, and so on. For this reason, we scrutinized the properties of our targeted essential hypothetical proteins where analyzing these EHPs from multidrug resistance bacteria can lead to the identification of new potential therapeutic solutions.\u003c/p\u003e \u003cp\u003eWe searched for essential hypothetical proteins (EHPs) among the 336 essential proteins of this bacterial strain to meet this need. Essential genes/proteins are those that are vital for a pathogen's survival and thereby analyzing their functions and metabolic pathways, crucial information that may be central to life can be retrieved. In this research, we discovered 18 EHPs for the first time that may provide valuable information about the pathogenesis, molecular mechanisms, and functions of this bacteria. Functional annotation is a prerequisite in understanding the pathogen metabolic pathways and the products that they synthesize for their survival in adverse conditions. Moreover, domain analysis, which is a basic, distinctive, and stable unit of a protein structure that is fiercely conserved during the evolutionary process, is crucial for further investigation [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, the function of a protein is directly or indirectly related to the subcellular localization [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The physicochemical properties of a protein depict a chemical assessment that shows the identity of chemical nature, physical hazards and to understand or predict molecular attributes. The combined analysis of the physicochemical properties helps to characterize the proteins annotated as hypothetical proteins from the genome of an opportunistic pathogen like \u003cem\u003eP. aeruginosa\u003c/em\u003e PAO1(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the PPI network has provided congruent meaningful insights into the protein\u0026rsquo;s function. Here, we have looked for potential relativity to our predicted function of EHPs and their connectivity with proteins involved with functionally important activities. The protein PA2986 (NP_251676.1) related to the MacB-like periplasmic core domain, represents a connection with 45 proteins of which 8 are hypothetical proteins (HP). A notable number of proteins grouped with PA2986 are involved in protein translocation activities such as translocation protein TolQ, TolR and TolB. Tol proteins show activity in gram-negative bacteria by providing stability to the outer membrane [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Moreover, ABC transporter ATP-binding protein (PA0073) is related to it as it functions by utilizing TolC exit duct by shifting substrates to extracellular space from the periplasm [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. This finding supports the idea of PA2986 being a member of the MacB-like periplasmic core domain. Another important protein for bacterial survival lysS, a lysine tRNA ligase was found to interact with PA2986 which is a mutant in some gram-negative bacteria conferring resistance against the OP0595, diazabicyclooctane b-lactamase inhibitor (an antibiotic) [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. We have found Penicillin-Binding Protein 1 called ponA protein in this group of networks. Alteration in the ponA protein ( penicillin-binding protein 1A) has a significant role in harnessing Chromosomally mediated resistance against penicillin in \u003cem\u003eN. gonorrhoeae\u003c/em\u003e [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Similarly, other considerable proteins like outer-membrane lipoprotein carrier protein lolA, transporter ExbB, penicillin-binding protein 1A (ponA) interacted with PA2986 Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePA0759 (NP_249450.1) has got the 2nd largest degree value having 41 nodes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in connection of which 17 are HPs. The highest betweenness centrality value of 17881.76 determines its significance towards cell signaling pathways as in the case of directed or regulated networks, Betweenness centrality is considered to be a much robust essentiality indicator than degree value [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Genes in this hub include proteins having prime roles in translational regulation and cellular metabolic activities such as glycine cleavage system protein T2 [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], translation elongation factor (tsf) [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], and ribosomal large subunit pseudouridine synthase C (rluC) [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], respectively. Moreover, RecO protein in this network is a replication repairing protein from the RecF recombination repair pathway that facilitates both DNA strand annealing and DNA recombination in complex with RecA protein found in high radiation tolerant bacteria \u003cem\u003eDeinococcus radiodurans\u003c/em\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. This property may also contribute to better survival efficacy for \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 in extreme conditions.\u003c/p\u003e \u003cp\u003eThe PA4562 (NP_253252.1) protein is a probable member of the Lipid II flippase MurJ family which is used for the genesis of lipid II on both inner and outer leaflets and that ultimately produces peptidoglycan in almost every bacterial species. Peptidoglycan is the primary protective foundation for shielding against environmental hazards and is involved in cell wall organizations [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Proteins related with morphological importance in bacteria such as flagellar basal body rod protein ( FlgC) [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], rod shape-determining protein (rodA) [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], type 4 fimbrial biogenesis outer membrane protein (PilQ) [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] are present in a connection with the PA4562 proteins that strengthen our prediction regarding this protein function.\u003c/p\u003e \u003cp\u003eThe proteins PA3685 (NP_252375.1) and PA3767 (NP_252456.1) are adjoined with 12 and 6 HPs respectively. Both of these proteins are largely involved with enzymes of different molecular functions-tRNA N6-adenosine threonyl carbamoyl transferase (gcp) is a universal structural modifier found at position 37 of tRNAs that provides the anticodon loop with greater binding efficiency to ribosomes invitro in \u003cem\u003eE. coli\u003c/em\u003e [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The protein UDP-2,3-diacyl glucosamine hydrolase (PA1792) is hypothesized to be catalyzing lipid-A biogenesis in \u003cem\u003eE. coli\u003c/em\u003e bacteria [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Lipid-A is a saccharolipid that modulates lipopolysaccharide (LPS) anchorage on the outer leaflet of the outer membrane in gram-negative bacteria which is an essential component for the bacteria shielding from antibiotics and sustain its viability [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Besides other proteins having enzymatic properties include thiamine monophosphate kinase (thiL), ATP-dependent DNA helicase DinG (PA1045), riboflavin-specific deaminase/reductase (ribD), amidotransferase(PA1742), acetyltransferase (PA2631) Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe majority of the proteins connected with PA4992 (NP_253679.1) from aldo/keto reductase family are uncharacterized proteins. The PA4167 protein has a contributing role as a source of carbon and energy for a large number of bacteria [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. The hub proteins PA4093 (NP_252782.1) and PA4992 (NP_253679.1) are interconnected through the intermediate protein PA4098, a probable short-chain dehydrogenase enzyme [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mgtE is an Mg transporter that represents a connection with the PA3481 (NP_252171.1) which can be an opportunistic inhibitor for the type III secretion system (T3SS). T3SS is a formidable toxin injected by \u003cem\u003eP. aeruginosa\u003c/em\u003e that can ultimately cause cell death into its host. The mgtE interrupts with the T3SS transcription regulation system by provoking rsmYZ gene transcription hence inhibits T3SS protein expression [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Interestingly another analogous protein, DNA polymerase II (polB) is associated with this same hub protein PA3481. PolB functions as a crucial candidate for repressing the translation process of master T3SS regulator ExsA. ExsA operates a major role in maintaining the regulatory cascade of T3SS. Thus affecting ExsA expression can prohibit T3SS toxin secretion process. Furthermore, S Chakravarty \u003cem\u003eet al.\u003c/em\u003e, found that T3SS transcription is attenuated when polB is overexpressed. Therefore, polB may act as a promising target for therapeutic interventions [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Besides, proteins responsible for exopolysaccharide biosynthesis and biofilm formation namely pslA [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] and pslD [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] are both members of the psl operon. The presence of such virulent protein types in this protein hub suggests PA3481 as a crucial protein involved in multiple virulence pathways in \u003cem\u003eP. aeruginosa\u003c/em\u003e. The PA4636 (NP_253326.1) protein harbors some of the virulent proteins like lptA and algQ that is required for the biogenesis of lipid bilayer in the outer membrane in \u003cem\u003eP. aeruginosa\u003c/em\u003e [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] and facilitates in developing a chronic infection in cystic fibrosis [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome notable mutual interactions also have been observed between two hub proteins like PA2986 and PA4562 where interrelated proteins include \u0026ndash; mraY, a potential target for antibiotic development is a crucial element for the bacterial cell wall synthesis [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]; opr86, an outer membrane protein found previously in all gram-negative bacteria. Likewise suggested as a potential drug target with significant therapeutic potential against \u003cem\u003eP. aeruginosa\u003c/em\u003e in earlier studies [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]; rpoH, a 32-kDa heat shock protein in \u003cem\u003eE. coli\u003c/em\u003e can also take part as a complementary for sigma factor during the increasing temperature in the environment as well as while starving [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]; PA5568 possess an inner membrane translocation subunit protein YidC which facilitates proteins to be passed onto inner membranes without the help of Sec translocase complex proteins [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]; ComL is a lipoprotein that facilitates the DNA transformation process in \u003cem\u003eN. gonorrhoeae\u003c/em\u003e [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Lastly, organic solvent tolerance protein OstA holds interaction simultaneously with the top 3 hub genes of maximum node connection. Concurrently, OstA is a protein of high molecular significance as it is found in almost all gram-negative bacteria and is involved in the bacterial envelope biogenesis process. A study by HC Chiu \u003cem\u003eet al.\u003c/em\u003e, found that OstA deficiency in \u003cem\u003eHelicobacter pylori\u003c/em\u003e causes sensitivity to organic solvent, impaired membrane permeability, and vulnerability to antibiotics [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. The function of the proteins in this network shows relational integrity with our predicted HPs. Knowing the protein\u0026rsquo;s function in a protein-protein interaction network can facilitate the process of discovering the proteins with unknown functions [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHerein, we analyzed these properties of our targeted essential hypothetical proteins where PBIT servers direct categorized all the non- homology features (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We have selected NP_249450.1 and NP_251676.1 respectively for being virulent determined by two of our tools with strong confidence scores. Targeting these virulent factors can limit the pathogenicity of \u003cem\u003eP. aeruginosa\u003c/em\u003e. Even antivirulence drugs insist a pathogen towards a weaker selection for resistance in them compared to antibiotics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, understanding the virulence factors and their role in pathogenesis can lead us to a new potential therapeutic solution. Besides, druggability analysis also confirmed that NP_249450.1 and NP_251676.1 can be a new and potential drug targets.\u003c/p\u003e \u003cp\u003eStructure prediction and quality assessment of the predicted structure are also parallelly important to evaluate the molecular and biological functions of a protein in cells for in-depth analysis and drug target identification [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Before structure prediction, the information of Alpha helix (Hh), Extended strand (Ee), Beta turn (Tt), or Random coil (Cc) helps to establish the secondary structure. That\u0026rsquo;s why the secondary structure was annotated to complete the structure related to all the information of our selected two proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cb\u003eand supplementary table 3\u003c/b\u003e). Thus, we further predicted the 3D structure of two EHPs (NP_249450.1 and NP_251676.1) and assessed the quality of these structures to decipher their unique conformation. The 3D structure and Ramachandran plot are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, respectively. The predicted structure's quality assessment parameters are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and the overall quality factor is shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Our predicted structure is accurate and reliable, according to Ramachandran plot analysis, since more than 90% of residues are considered the cutoff value and our findings surpass that range by more than \u003cb\u003e94%.\u003c/b\u003e Therefore, this structural and functional information will open a new window for further identification of potential drug candidates that can halt the surge of this pathogenic bacterium from becoming resistant.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUnveiling the functional characterization of pathogenic microorganisms is of great importance in biological processes and medical science. Essential proteins and essential hypothetical proteins are versatile macromolecules that can be crucial in inferring new treatment strategies towards these pathogenic bacteria. For functional characterization of EHPs, we used an \u003cem\u003ein-silico\u003c/em\u003e approach in combination with different bioinformatics databases/tools, with ROC analysis indicating that these tools are highly reliable for functional characterization of \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01. We attributed function to 18 EHPs and analyzed subcellular localization and physiochemical properties of these proteins. Afterward, a PPIs network analysis was carried out on 9 stable EHPs and their functionally related proteins from this bacterium. Further, host non-homologous analysis predicts 5 pathogen-specific proteins, three of which have virulent factors that could be used as novel therapeutic targets. Finally, the structural conformation of two EHPs was determined, and the accuracy of the predicted model was evaluated, indicating that this model is highly accurate. Our findings will pave the way for new antibacterial drugs and treatment strategies to be developed by focusing on these novel drug targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was\u0026nbsp;no significant funding support for this investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlockgether J, T\u0026uuml;mmler B (2017) Recent advances in understanding Pseudomonas aeruginosa as a pathogen. F1000Research, p 6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiggle SP, Whiteley M, Profile M (2020) Pseudomonas aeruginosa: opportunistic pathogen and lab rat. Microbiology 166(1):30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikolaidis M, Mossialos D, Oliver SG et al (2020) Comparative Analysis of the Core Proteomes among the Pseudomonas Major Evolutionary Groups Reveals Species-Specific Adaptations for Pseudomonas aeruginosa and Pseudomonas chlororaphis. Diversity 12(8):289\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStover CK, Pham XQ, Erwin A et al (2000) Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen. Nature 406(6799):959\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePachori P, Gothalwal R, Gandhi P (2019) Emergence of antibiotic resistance Pseudomonas aeruginosa in intensive care unit; a critical review, vol 6. Genes \u0026amp; diseases, pp 109\u0026ndash;119. 2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubern JF, Cigana C, De Simone M et al (2015) Integrated whole-genome screening for P seudomonas aeruginosa virulence genes using multiple disease models reveals that pathogenicity is host specific. Environ Microbiol 17(11):4379\u0026ndash;4393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzam MW, Khan AU (2019) Updates on the pathogenicity status of Pseudomonas aeruginosa. Drug discovery today. 24:350\u0026ndash;3591\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeriasamy S, Nair HA, Lee KW et al (2015) Pseudomonas aeruginosa PAO1 exopolysaccharides are important for mixed species biofilm community development and stress tolerance. Front Microbiol 6:851\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleheck D, Barraud N, Klebensberger J et al (2009) Pseudomonas aeruginosa PAO1 preferentially grows as aggregates in liquid batch cultures and disperses upon starvation. PLoS ONE 4(5):e5513\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlockgether J, Cramer N, Wiehlmann L et al (2011) Pseudomonas aeruginosa genomic structure and diversity. Front Microbiol 2:150\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrava J, Pranavathiyani G, Pan A (2018) Functional assignment for essential hypothetical proteins of Staphylococcus aureus N315. Int J Biol Macromol 108:765\u0026ndash;774\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo H, Lin Y, Gao F et al (2014) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 42(D1):D574\u0026ndash;D580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraujo FA, Barh D, Silva A et al (2018) GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Scientific reports. 8:1\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeer LY, Domrachev M, Lipman DJ et al (2002) CDART: protein homology by domain architecture. Genome Res 12(10):1619\u0026ndash;1623\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLetunic I, Khedkar S, Bork P (2021) SMART: recent updates, new developments and status in 2020. Nucleic acids research. 49:D458\u0026ndash;D460D1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGough J, Karplus K, Hughey R et al (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 313(4):903\u0026ndash;919\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMistry J, Chuguransky S, Williams L et al (2021) Pfam: The protein families database in 2021. Nucleic acids research. 49:D412\u0026ndash;D419D1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi YH, Xu JY, Tao L et al (2016) SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS ONE 11(8):e0155290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSillitoe I, Bordin N, Dawson N et al (2021) CATH: increased structural coverage of functional space. Nucleic Acids Res 49(D1):D266\u0026ndash;D273\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlum M, Chang H-Y, Chuguransky S et al (2021) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49(D1):D344\u0026ndash;D354\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabler F, Nam SZ, Till S et al (2020) Protein Sequence Analysis Using the MPI Bioinformatics Toolkit. Curr Protocols Bioinf 72(1):e108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoskinen P, T\u0026ouml;r\u0026ouml;nen P, Nokso-Koivisto J et al (2015) PANNZER: high-throughput functional annotation of uncharacterized proteins in an error-prone environment. Bioinformatics 31(10):1544\u0026ndash;1552\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawkins T, Chitale M, Luban S et al (2009) PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data, vol 74. Structure, Function, and Bioinformatics, Proteins, pp 566\u0026ndash;582. 3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChitale M, Hawkins T, Park C et al (2009) ESG: extended similarity group method for automated protein function prediction. Bioinformatics 25(14):1739\u0026ndash;1745\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu NY, Wagner JR, Laird MR et al (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26(13):1608\u0026ndash;1615\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu CS, Lin CJ, Hwang JK (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n‐peptide compositions. Protein science. 13:1402\u0026ndash;14065\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu CS, Chen YC, Lu CH et al (2006) Prediction of protein subcellular localization. Proteins Struct Funct Bioinform 64(3):643\u0026ndash;651\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrogh A, Larsson B, Von Heijne G et al (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567\u0026ndash;580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026auml;ll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338(5):1027\u0026ndash;1036\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTusnady GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17(9):849\u0026ndash;850\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobson L, Rem\u0026eacute;nyi I, Tusn\u0026aacute;dy GE (2015) CCTOP: a Consensus Constrained TOPology prediction web server. Nucleic Acids Res 43(W1):W408\u0026ndash;W412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmasits U, Ahrens CH, M\u0026uuml;ller S et al (2014) Protter: interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 30(6):884\u0026ndash;886\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen H \u003cem\u003ePredicting secretory proteins with SignalP\u003c/em\u003e, in \u003cem\u003eProtein function prediction\u003c/em\u003e2017,Springer. p.59\u0026ndash;73\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiller K, Grote A, Scheer M et al (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res 32(suppl2):W375\u0026ndash;W379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanapathiraju M, Balakrishnan N, Reddy R et al (2008) Transmembrane helix prediction using amino acid property features and latent semantic analysis. Bmc Bioinformatics. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGasteiger E, Gattiker A, Hoogland C et al (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31(13):3784\u0026ndash;3788\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaaz M, ImtaiyazHassan M, Ahmad F (2013) Functional annotation of conserved hypothetical proteins from Haemophilus influenzae Rd KW20. PloS one. 8:e8426312\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaqvi AAT, Shahbaaz M, Ahmad F et al (2015) Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum PloS one 10(4):e0124177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou G, Soufan O, Ewald J et al (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47(W1):W234\u0026ndash;W241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u0026ndash;2504\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShende G, Haldankar H, Barai RS et al (2017) Pipeline Builder for Identification of drug Targets for infectious diseases. Bioinformatics 33(6):929\u0026ndash;931\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValdes AM, Walter J, Segal E et al (2018) Role of the gut microbiota in nutrition and health.Bmj,361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha S, Raghava G (2006) VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition, vol 4. Genomics, proteomics \u0026amp; bioinformatics, pp 42\u0026ndash;47. 1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarg A, Gupta D (2008) VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 9(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta A, Kapil R, Dhakan DB et al (2014) MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. PLoS ONE 9(4):e93907\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmmerich CH, Gamboa LM, Hofmann MC et al (2020) Improving target assessment in biomedical research: the GOT-IT recommendations.Nature Reviews Drug Discovery, : p.1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074\u0026ndash;D1082\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681\u0026ndash;684\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuchan DW, Jones DT (2019) The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res 47(W1):W402\u0026ndash;W407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaterhouse A, Bertoni M, Bienert S et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296\u0026ndash;W303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Anishchenko I, Park H et al (2020) Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences, 117(3): p.\u0026nbsp;1496\u0026ndash;1503\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeShin W (2014) Prediction of protein structure and interaction by GALAXY protein modeling programs. Biodesign 2:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511\u0026ndash;1519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164\u0026ndash;170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml;thy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83\u0026ndash;85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264(1):121\u0026ndash;136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooft RW, Vriend G, Sander C et al (1996) Errors in protein structures. Nature 381(6580):272\u0026ndash;272\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaskowski RA, MacArthur MW, Moss DS et al (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283\u0026ndash;291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition. 30:1145\u0026ndash;11597\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEng J (2017) \u003cem\u003eROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University [updated 2014 March 19]\u003c/em\u003e, Accessed 25/05/17. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www\u003c/span\u003e\u003cspan address=\"http://www\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. jrocfit. org\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuk AC, Mashalidis EH, Lee S-Y (2017) Crystal structure of the MOP flippase MurJ in an inward-facing conformation. Nat Struct Mol Biol 24(2):171\u0026ndash;176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MS, Shahik SM, Sohel M et al (2015) In silico structural and functional annotation of hypothetical proteins of Vibrio cholerae O139, vol 13. Genomics \u0026amp; informatics, p 53. 2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalhotra H, Kaur H (2016) A bioinformatics approach for functional and structural analysis of hypothetical proteins of Clostridium difficile. Imp J Interdiscip Res 2:1601\u0026ndash;1609\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkai A (1980) Thermostability and aliphatic index of globular proteins. J Biochem 88(6):1895\u0026ndash;1898\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Costa WLO, Ara\u0026uacute;jo CLdA, Dias LM et al (2018) Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance. PLoS ONE 13(6):e0198965\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuruprasad K, Reddy BB, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng Des Selection 4(2):155\u0026ndash;161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105\u0026ndash;132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia J, Benner MJ, Hancock RE (2014) NetworkAnalyst-integrative approaches for protein\u0026ndash;protein interaction network analysis and visual exploration. Nucleic Acids Res 42(W1):W167\u0026ndash;W174\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng L, Liu H, Lan T et al (2020) Antibiotic Resistance Patterns of Pseudomonas spp. Isolated From Raw Milk Revealed by Whole Genome Sequencing. Front Microbiol 11:1005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoole K (2011) Pseudomonas aeruginosa: resistance to the max. Front Microbiol 2:65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman A, Susmi TF, Yasmin F et al (2020) Functional annotation of an ecologically important protein from Chloroflexus aurantiacus involved in polyhydroxyalkanoates (PHA) biosynthetic pathway. SN Appl Sci 2(11):1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItzhak DN, Tyanova S, Cox J et al (2016) Global, quantitative and dynamic mapping of protein subcellular localization. elife. 5:e16950\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamsonov GV (2012) Handbook of the Physicochemical Properties of the Elements. Springer Science \u0026amp; Business Media\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazzaroni J-C, Dubuisson J-F, Vianney A (2002) The Tol proteins of Escherichia coli and their involvement in the translocation of group A colicins, vol 84. Biochimie, pp 391\u0026ndash;397. 5\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrow A, Greene NP, Kaplan E et al (2017) Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily. Proceedings of the National Academy of Sciences, 114(47): p.\u0026nbsp;12572\u0026ndash;12577\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoumith M, Mushtaq S, Livermore D et al (2016) New insights into the regulatory pathways associated with the activation of the stringent response in bacterial resistance to the PBP2-targeted antibiotics, mecillinam and OP0595/RG6080. J Antimicrob Chemother 71(10):2810\u0026ndash;2814\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRopp PA, Hu M, Olesky M et al (2002) Mutations in ponA, the gene encoding penicillin-binding protein 1, and a novel locus, penC, are required for high-level chromosomally mediated penicillin resistance in Neisseria gonorrhoeae. Antimicrob Agents Chemother 46(3):769\u0026ndash;777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H, Kim PM, Sprecher E et al (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3(4):e59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;ller M, Papadopoulou B (2010) Stage-specific expression of the glycine cleavage complex subunits in Leishmania infantum. Molecular and biochemical parasitology. 170:17\u0026ndash;271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn G, Bendiak DS, Mamelak LA et al (1981) Organization and nucleotide sequence of a new ribosomal operon in Escherichia coli containing the genes for ribosomal protein S2 and elongation factor Ts, vol 9. Nucleic acids research, pp 4163\u0026ndash;4172. 16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConrad J, Sun D, Englund N et al (1998) The rluC gene of Escherichia coli codes for a pseudouridine synthase that is solely responsible for synthesis of pseudouridine at positions 955, 2504, and 2580 in 23 S ribosomal RNA. J Biol Chem 273(29):18562\u0026ndash;18566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakharashvili N, Koroleva O, Bera S et al (2004) A novel structure of DNA repair protein RecO from Deinococcus radiodurans. Structure 12(10):1881\u0026ndash;1889\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng S, Sham L-T, Rubino FA et al (2018) Structure and mutagenic analysis of the lipid II flippase MurJ from Escherichia coli. Proceedings of the National Academy of Sciences, 115(26): p.\u0026nbsp;6709\u0026ndash;6714\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuberi AR, Ying C, Bischoff DS et al (1991) Gene-protein relationships in the flagellar hook-basal body complex of Bacillus subtilis: sequences of the flgB, flgC, flgG, fliE and fliF genes. Gene 101(1):23\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuzawa H, Asoh S, Kunai K et al (1989) Nucleotide sequence of the rodA gene, responsible for the rod shape of Escherichia coli: rodA and the pbpA gene, encoding penicillin-binding protein 2, constitute the rodA operon. J Bacteriol 171(1):558\u0026ndash;560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin PR, Hobbs M, Free PD et al (1993) Characterization of pilQ, a new gene required for the biogenesis of type 4 fimbriae in Pseudomonas aeruginosa. Molecular microbiology. 9:857\u0026ndash;8684\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Yacoubi B, Lyons B, Cruz Y et al (2009) The universal YrdC/Sua5 family is required for the formation of threonylcarbamoyladenosine in tRNA. Nucleic Acids Res 37(9):2894\u0026ndash;2909\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabinski KJ (2004) Genetic and biochemical characterization of the specific UDP-2, 3-diacylglucosamine hydrolase of lipid A biosynthesis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetzger LE, Lee JK, Stroud RM et al (2010) Discovery, characterization, and structural determination of a novel UDP-2, 3‐diacylglucosamine hydrolase. Wiley Online Library\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYum D-Y, Lee B-Y, Pan J-G (1999) Identification of the yqhE andyafB Genes Encoding Two 2, 5-Diketo-d-Gluconate Reductases inEscherichia coli. Appl Environ Microbiol 65(8):3341\u0026ndash;3346\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoynie L, Schnell R, McMahon SA et al (2013) The AEROPATH project targeting Pseudomonas aeruginosa: crystallographic studies for assessment of potential targets in early-stage drug discovery. Acta Crystallographica Section F: Structural Biology and Crystallization Communications, 69(1): p.\u0026nbsp;25\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakravarty S, Melton CN, Bailin A et al (2017) Pseudomonas aeruginosa magnesium transporter MgtE inhibits type III secretion system gene expression by stimulating rsmYZ transcription.Journal of bacteriology,199(23)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakravarty S, Ramos-Hegazy L, Gasparovic A et al (2020) DNA alternate polymerase PolB mediates inhibition of type III secretion in Pseudomonas aeruginosa.Microbes and Infection, : p.104777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverhage J, Schemionek M, Webb JS et al (2005) Expression of the psl operon in Pseudomonas aeruginosa PAO1 biofilms: PslA performs an essential function in biofilm formation. Appl Environ Microbiol 71(8):4407\u0026ndash;4413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampisano A, Schroeder C, Schemionek M et al (2006) PslD is a secreted protein required for biofilm formation by Pseudomonas aeruginosa. Appl Environ Microbiol 72(4):3066\u0026ndash;3068\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperandeo P, Cescutti R, Villa R et al (2007) Characterization of lptA and lptB, two essential genes implicated in lipopolysaccharide transport to the outer membrane of Escherichia coli. J Bacteriol 189(1):244\u0026ndash;253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonyecsni W, Deretic V (1990) DNA sequence and expression analysis of algP and algQ, components of the multigene system transcriptionally regulating mucoidy in Pseudomonas aeruginosa: algP contains multiple direct repeats. J Bacteriol 172(5):2511\u0026ndash;2520\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung BC, Zhao J, Gillespie RA et al (2013) Crystal structure of MraY, an essential membrane enzyme for bacterial cell wall synthesis. Science 341(6149):1012\u0026ndash;1016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTashiro Y, Nomura N, Nakao R et al (2008) Opr86 is essential for viability and is a potential candidate for a protective antigen against biofilm formation by Pseudomonas aeruginosa. J Bacteriol 190(11):3969\u0026ndash;3978\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkins DE, Auger EA, Matin A (1991) Role of RpoH, a heat shock regulator protein, in Escherichia coli carbon starvation protein synthesis and survival. J Bacteriol 173(6):1992\u0026ndash;1996\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuelson JC, Chen M, Jiang F et al (2000) YidC mediates membrane protein insertion in bacteria. Nature 406(6796):637\u0026ndash;641\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFussenegger M, Facius D, Meier J et al (1996) A novel peptidoglycan-linked lipoprotein (ComL) that functions in natural transformation competence of Neisseria gonorrhoeae. Molecular microbiology. 19:1095\u0026ndash;11055\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu HC, Lin TL, Wang JT (2007) Identification and characterization of an organic solvent tolerance gene in Helicobacter pylori. Helicobacter 12(1):74\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao VS, Srinivas K, Sujini G et al (2014) Protein-protein interaction detection: methods and analysis. International journal of proteomics, 2014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImam N, Alam A, Ali R et al (2019) In silico characterization of hypothetical proteins from Orientia tsutsugamushi str. Karp uncovers virulence genes. Heliyon 5(10):e02734\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pseudomonas aeruginosa, Functional Annotation, Protein-protein Interactions, Non-homology analysis, Therapeutics","lastPublishedDoi":"10.21203/rs.3.rs-1650735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1650735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PAO1, an omnipresent opportunistic bacterium responsible for acute and chronic infection in immunocompromised individuals, is currently on WHO's lists where new antibiotics are urgently required for those. Finding essential genes and essential hypothetical proteins(EHP) can be crucial in identifying novel druggable targets and therapeutics. This study aims to characterize these EHPs, analyze subcellular and physiochemical properties, PPIs network, non-homologous analysis against humans, virulence factor and novel drug target prediction, and finally structural analysis of the identified target employing around 42 robust bioinformatics tools/databases, the output of which was evaluated using ROC analysis. The study discieverd 18 EHPs from 336 essential genes, with domain and functional annotation revealing that 50% of these proteins belong to the enzyme category. The majority are cytoplasmic and cytoplasmic membrane proteins, with half of them being stable proteins which were subjected to PPIs network analysis. The network contains 261 nodes and 269 edges for 9 proteins of interest, with 11 hubs containing at least three nodes each. Finally, pipeline builder predicts 7 proteins with novel drug targets, 5 non-homologous proteins against human proteome, human anti-targets, human gut flora, and 3 virulent proteins. Among these, homology modeling of NP_249450 and NP_251676 were done and the Ramachandran plot analysis revealed that more than 94% of the residues were in the preferred region. By analyzing functional attributes and virulence characteristics, the findings of this study may facilitate the development of innovative antibacterial drug targets and drugs of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PAO1.\u003c/p\u003e","manuscriptTitle":"Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An in silico Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-26 15:51:58","doi":"10.21203/rs.3.rs-1650735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f7bb85f-5241-4934-8ba3-0fd52277d5b2","owner":[],"postedDate":"May 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-31T20:04:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-26 15:51:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1650735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1650735","identity":"rs-1650735","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}