Proteomics based systematic exploration of the peptidoglycan biosynthesis of Olsenella uli DSM 7084 towards pathogenesis

Proteomics based systematic exploration of the peptidoglycan biosynthesis of Olsenella uli DSM 7084 towards pathogenesis | 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 Proteomics based systematic exploration of the peptidoglycan biosynthesis of Olsenella uli DSM 7084 towards pathogenesis Mohammad Salman Akhtar, Arshi Talat, Tulika Bhardwaj, Mansoor Alsahag, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4456653/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 The oral microbiota plays a pivotal role in either promoting health or exacerbating disease progression. Within the diverse microbial community, Olsenella uli emerges as a concerning pathogen linked to various endodontic infections. Advancements in next-generation sequencing methods and bioinformatics have begun unraveling the intricate nature of the oral microbiome. Understanding these oral microorganisms opens doors to exploring functional and metabolic changes, offering valuable insights for drug development and targeted therapies. Consequently, our current investigation employs a comparative subtractive proteomics approach to identify potential drug targets within Olsenella uli DSM 7084. This effort unveils eight promising drug target candidates, which undergo thorough assessment for druggability and sub-cellular localization. Furthermore, molecular docking simulations involving these prioritized targets and FDA-approved drugs establish a foundational framework for future researchers, expediting the drug development process aimed at combating infections caused by this formidable pathogen. Our research intends to accomplish precision drug target discovery using an integrated method that integrates subtractive proteome analysis, systems biology, and molecular docking. This method paves the path for more precise molecular docking investigations by enabling a thorough understanding of prospective pharmacological targets. Oral infections Olsenella uli subtractive proteomics druggability analysis physio-chemical characterization molecular docking Figures Figure 1 1. Introduction Oral diseases such as tooth decay, periapical periodontitis, and endodontics have evolved into serious global public health problems. Environmental, biological, and lifestyle-related risk factors all contribute to dental caries [ 1 ]. In terms of biological aspects, oral microflora found in the crowns and roots of teeth play a significant influence in oral health [ 2 ]. The mutans streptococci, Porphyromonas, Olsenella sp. and Prevotella intermedia particularly Streptococcus mutans (S. mutans), Olsenella uli and Streptococcus sobrinus , are the main cause of coronal and root caries [ 3 , 4 ]. Some acid-tolerant strains, including Streptococcus sanguis and Streptococcus gordonii are also suspected of being responsible for coronal caries. Olsenella uli is one out of thirteen genera in the family Coriobacteriaceae [ 5 ]. In root canal samples collected after chemo-mechanical preparation and intracanal medication, O. uli was discovered to predominate over other Gram-positive rods, such as Atopobium parvulum , indicating that this species can resist intracanal disinfection procedures and may be responsible for persistent infections. It is a gram-positive bacterium from the genus Olsenella which was isolated from the gingival cervices and periodontal pockets of human [ 6 ]. Olsenella uli DSM 7084 (NCBI Accession Number:NC_014363.1) is a double stranded genome of 2.05 Mb size and 64.7 GC% consisting of 1742 proteins, 1807 genes, 49 tRNA and 3 rRNA. The traditional drug discovery process takes approximately ten to fifteen years to introduce a drug to market in the traditional drug discovery system [ 7 ]. Drug target identification in the bench-top methods is time-consuming and expensive. As a result, drug target identification using a computational-based bioinformatics approach is the most preferable method because it is time and money efficient [ 8 ]. Owing to the availability of omics data, systematic identification of prioritized drug targets is easily achieved. Several studies indicate the potential of subtractive proteome screening towards the identification of drug targets for Clostridium botulinum [ 9 ], Mycobacterium tuberculosis [ 10 ], Klebsiella [ 11 ], Helicobacter pylori [ 12 ] etc. Protein-protein network analysis aids in the identification and characterization of promising drug targets of virulent uropathogen Escherichia coli strain CFT073 [ 13 ]. Metabolic pathways are considered attractive targets for drug target identification as they constitute the core proteome of genome which is indispensable for cell survival. Targeting such proteome results in the eradication of infection caused by the organism [ 14 ]. Therefore, in present study, metabolic pathways of host and pathogen Olsenella uli DSM 7084 are targeted to identify potential pathogen specific proteins. Further, subtractive proteome screening via druggability analysis, sub cellular localization, physio-chemical characterization enables the identification of potential drug targets of pathogen and FDA approved drugs to be utilized in future to treat the infections caused by query pathogen. 2. Materials and Methodology 2.1 Primary dataset collection Comparative genome analysis The publically available genomes of the Olsenella species were obtained in fasta and GenBank format from genome database of NCBI (Table). EDGAR 3.0, a user-friendly platform enabling genome-level comparison and evolutionary tracking among sequenced strains was utilized for pangenome composition. The core genome is computed along with phy The proteome information about the metabolic pathways of pathogen Olsenella uli DSM 7084 and host Homo sapiens were iterated from Kyoto Encyclopedia of Genes and Genomes [ 15 ]. It is knowledge database for systematic analysis of gene functions, genomic information with functional information. The comparative analysis was manually performed to identify the strain specific metabolic pathway present in the pathogen only. The corresponding protein sequences of enzymes involved in unique (strain specific) pathways were iterated from KEGG database in FASTA format. 2.2 Non-homologous essential protein selection The non-homology analysis of proteome dataset from unique metabolic pathways was completed in two phases. In phase I, sequence similarity search was performed for mined dataset against host ( Homo sapiens ) proteome utilizing BLASTP based on a threshold expectation value (e-value) of 0.005, matching similarity of ≤ 35%, and minimum bit score of 100. In phase II, screened non-homologous protein sequences from Phase I were subjected to essentiality analysis against DEG microbial BLASTP with a cutoff e-value of 10 − 10 and a least possible bit score of 100. 2.3 Physio-chemical characterization of non-homologous essential proteins Physio-chemical characterization enables the better understanding of aliphatic index, thickness and stability of drug target. ProtParam [ 16 ] is a tool which allows the computation of physical and chemical parameters for a given protein sequence i.e. isoelectric point, molecular weight, number of positively and negatively charged residues, instability index, aliphatic index, extinction coefficient, GRAVY (Grand average of hydropathicity). The instability index provides an estimate of the stability of your protein in a test tube. The aliphatic index is an important factor for analysing the thermostability of globular proteins by defining the relative volume occupied by aliphatic side chains (alanine, valine, isoleucine, and leucine). It is computed by formula: Aliphatic Index = X(Ala) + a * X(Val) + b * (X(Ile) + X(Leu)) where, X(Ala), X(Val), X(Ile) and X (Leu) are mole percent of Alanine, valine, Isoleucine and Leucine The coefficients of a and b = relative volume of valine side chains and Leu/Ile to the side chains of alanine (a = 2.9 and b = 3.9) The ratio of sum of hydropathy values of all amino acids by the number of residues in the sequence is defined as Grand average of hydropathicity. 2.4 Druggability analysis of stable non-homologous essential proteins The druggability of the identified essential proteins was assessed using DrugBank (ver. 4.3) [ 17 ], which provides unique bioinformatic and cheminformatic data on drugs and drug targets. The FDA-approved small molecule drugs, biotech (protein/peptide) drugs, nutraceuticals, and experimental drugs were among the drug entries that were used to align the proteins using the default parameters. The resultant hits found with the DrugBank database were defined as druggable targets, whereas the non-hits were considered unique targets and needed to be evaluated experimentally. 2.5 Sub-cellular localisation of druggable targets Protein's sub-cellular localization is a critical step in identifying it as a potential therapeutic target. This leads to a better understanding of each protein's function, which aids in distinguishing between targets for antimicrobial agents and those for vaccine therapy [ 18 ]. CELLO v.2.5 (multi-class support vector machine classification system) [ 19 ] predicts the biological significance and subcellular localization of the proteins. 2.6 Three-dimensional protein structure generation The protein sequences of all screened drug targets were mined from KEGG database in FASTA format. Further, I-TASSER was utilized to generate three-dimensional structure of all screened target protein sequences. The iterative threading assembly refinement (I-TASSER) is a unified platform for automated structure generation and functional prediction utilizing multiple threading algorithms and iterative structural assembly simulations. Based on pair-wise structural similarity, C-score was generated for each iteration. C-score is computed based significance of threading template alignments and the convergence parameters of the structure assembly simulations which ranges between − 5 and 2. Further, high C-score signifies a model of high confidence [ 20 ]. 2.7 Molecular Docking An automated flexible docking suite, AutoDock v 4.2 [ 21 ] was utilized for performing in-silico molecular docking. Drugs iterated from DrugBank in SDF format for the concerned drug target were utilized for docking. PDB files were converted into PDBQT files by assigning Kollman charges and hydrogen atoms [ 22 , 23 , 24 ]. A grid of 70 x 70x 70 for x, y and z coordinates, respectively were taken and centered at the binding pockets identified by PockDrug software [ 25 , 26 ]. To explore the optimized conformational, positional thresholds and maximum possible orientations, a total number of 30 runs were considered to compute the binding energies using Lamarckian genetic algorithm at default parameters [ 27 ]. Binding energies were computed based on the adaptive local search global optimization algorithm. PyMol ( https://pymol.org ) enables the visualization of docked complexes. 3. Results 3.1 Dataset collection The information about metabolic pathways of pathogen and host was mined from the KEEG database This includes KEDD ID of metabolic pathways, pathway name for both the genomic organism. Manual curation of comparative analysis results in the identification of 21 unique (strain-specific) pathways (Supplementary File : Table 1 ). The protein sequences of all the genes participating in unique pathways were mined from NCBI in FASTA format (Supplementary File : Table 2 ). 3.2 Non-homologous essential protein selection BLASTp enables the similarity search analysis of screened proteome dataset of unique pathways against the Database of Essential Genes (DEG) which is a collection of important genes that can be discovered in a variety of pathogenic and non-pathogenic species (both pro- and eukaryotes). A total of 14 proteins were identified as essential proteins required for the viability of Olsenella uli DSM 7084 and subjected to further screening prior considered as potent drug targets ( Supplementary File: Table 3 ). 3.3 Physio-chemical characterization of non-homologous essential proteins In-silico physio chemical characterisation enables the computation of molecular weight, aliphatic index, instability index and grand average value of hydropathicity. The computation of an amino acid sequence's isoelectric point (theoretical pI) and molecular weight (Mw) is beneficial because these data dictate the approximate area of a 2D-gel where a protein of interest may be detected. Most proteins are acidic or nearly neutral in nature with isoelectric point ranging from (4.66 to 6.58). with Aliphatic index define the thermal stability of proteins. Aliphatic amino acids are hydrophobic on nature The aliphatic index of screened non-homologous essential proteins in the range of 70 to 102.64 indicates that these proteins are thermally stable as well as they contain high amount of hydrophobic amino acids. It results in the amphipathic nature of proteins due to the copresence of hydrophobic and polar (charged) residues [ 28 ]. Instability index shows that among 14 screened targets, 8 are stable in nature (instability index < 40). Therefore, in this study only stable targets are considered for further subtractive screening of drug compounds ( Supplementary File: Table 4). 3.4 Druggability analysis The druggability analysis of a protein is an important step in drug target identification that was assessed based on the assumption that druggable protein targets should interact with the drug-like compound. Therefore, the DrugBank database was used to identify the drug targets. Eight essential non-homologous stable proteins that were found in the prediction of physio-chemical characterization step were subjected to the DrugBank database against BLASTp search with default settings. DrugBank hits were classified as common targets or druggable targets, while the remaining hits were categorised as novel drug targets that are further encouraged for experimental validation (Supplementary File: Table 5). 3.5 Subcellular localisation of druggable targets For identification of the biological significance of its function and subcellular localization, all predicted non-homologous druggable stable were subjected to subcellular localization prediction. The resultant output from CELLO v.2.5 reveals that majority of the screened proteins were present in the cytoplasm (Supplementary File: Table 6). The proteins that reside in Cytoplasmic might be an attractive drug target [ 29 ]. 3.6 Three-dimensional protein structure generation The homology modelling of possible drug targets was carried out by performing I-TASSER, a commonly used and precise computer program for predicting the three-dimensional structure of the ‘target’ protein. Models generated from I-TASSER have already undergone quality assessment and predicts the final optimal model by means of C-score. The resultant C-score of selected models for eight screened proteins are listed in Table 1 . Table 1 Enlisting C-score of best optimal model generated for screened 8 potential drug targets S.No. Drug Targets C-score 1. S-ribosylhomocysteine lyase 0.1 ± 0.31 2. UDP-N-acetylglucosamine–N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase 0.45 3. alanine racemase 0.32 ± 0.11 4. UDP-N-acetylmuramoylalanine–D-glutamate ligase 0.71 ± 0.12 5. UDP-N-acetylglucosamine 1-carboxyvinyltransferase 0.53 6. 4-alpha-glucanotransferase 1.21 7. beta-glucosidase 0.82 ± 0.11 8. aspartate-semialdehyde dehydrogenase 0.62 3.7 Molecular Docking The intermolecular interactions among proteins and ligands were analyzed for computing binding energies of protein-ligand complexes using AutoDock v 4.2 ( Supplementary File: Table 7) . Three docked complexes with minimum binding energy were selected for detailed analysis in this study (Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine, {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid and Uridine-Diphosphate-N-Acetylglucosamine for UDP-N-acetylmuramoylalanine–D-glutamate ligase, alanine racemase and UDP-N-acetylglucosamine-1-carboxyvinyltransferase respectively).PubChem database was mined for the retrieval of chemical structures of the prioritized drugs. The binding interactions of docked complexes are depicted in Fig. 1 . Uridine-Diphosphate-N-Acetylglucosamine was found to be most effective in inhibiting UDP-N-acetylglucosamine-1-carboxyvinyltransferase with a binding energy of -9.66 kcal/mol followed by Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine for UDP-N-acetylmuramoylalanine–D-glutamate ligase with − 9.16 kcal/mol and {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid for alanine racemase with − 8.76 kcal/mol binding energy (Table 2 ). Table 2 Computed binding energies of the docked complexes Compound Binding energy (kcal mol − 1 ) Docked energy (kcal mol − 1 ) Inter molecular energy (kcal mol − 1 ) Torsional energy (kcal mol − 1 ) Internal energy (kcal mol − 1 ) RMSD (Å) Complex 1 -8.76 -4.10 -3.09 0.77 -0.1 70.263 Complex 2 -9.16 -3.15 -3.12 0.45 0.05 62.44 Complex 3 -9.66 -2.99 -3.12 0.34 -0.17 76.99 4. Discussion Bacteremia is well documented following dental procedures such as tooth extraction, endodontic treatment, periodontal surgery, and root scaling [ 30 ]. The protective function of the oral epithelium over the underlying tissues, the antibacterial properties of saliva, and the immune responses of the phagocytes all contribute to the rarity of primary bacterial infections of the oral mucosa [ 31 ]. The risk of primary bacterial infections increases if the oral mucosa is damaged by poor oral hygiene, trauma, smoking and alcohol abuse. Major bacterial agents responsible for endodontic infections are Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, Olsenella uli and Prevotella intermedia. Typically found in the mouth or gastrointestinal tract, Olsenella uli is an anaerobic or microaerophilic bacteria. In the human mouth, lesions such as gingival and subgingival sites with periodontitis are most often places from where Olsenella uli is isolated [ 5 ]. Olsenella uli DSM 7084 genome has been found to have a number of distinct metabolic pathways that do not exist in their natural host. These distinct pathways make it possible to find antimicrobials that target the pathogen specifically and are therefore safe. Furthermore, non homology essentiality analysis enables the identification of non-homologous genome content against host proteome which is indispensable for pathogen survival. Non-homology analysis diminishes the changes of any drug side-effect and cross-reactivity [ 32 ]. Designing effective therapeutic agents with the objective of inhibiting the pathogen's survival and/or replication has an added bonus when essential genes and proteins in particular metabolic pathways are targeted [ 33 ]. A protein's cellular localization must be identified before it can be considered a potential target for therapeutic intervention to clearly differentiate between targets for antimicrobial agents and those for vaccine therapy [ 34 ]. In support, physio-chemical characterization provides the platform for future bench-top experiments [ 18 ]. Additionally, the ability of FDA-approved, nutraceutical, or experimental drugs to bind proteins similar to the identified pathogen essential proteins shows that these proteins may be druggable as therapeutic targets [ 35 ] and opens the door to the use of various drug combinations to treat Olsenella uli DSM 7084 infections in human. The present subtractive proteome screening of strain specific pathways of Olsenella uli DSM 7084 renders 8 potential drug targets (Table 4). These screened targets are considered indispensable for pathogen survival therefore serve as precursor for drug discovery and vaccine development. In this study, 8 potential drug targets were subjected to molecular docking with their screened FDA approved drugs to identify top three docked complexes with minimum binding energy i.e. (Complex 1)UDP-N-acetylmuramoylalanine–D-glutamate ligase-Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine, (Complex 2) alanine racemase-{1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid and (Complex 3) UDP-N-acetylglucosamine-1-carboxyvinyltransferase-Uridine-Diphosphate-N-Acetylglucosamine of -9.16 kcal/mol,-8.76 kcal/mol and − 9.66 kcal/mol binding energies respectively (Table 2 ). UDP-N-acetylmuramoylalanine–D-glutamate ligase is a cytoplasmic enzyme involved in the catalytic addition of meso -diaminopimelic acid to nucleotide precursor UDP- N -acetylmuramoyl- l -alanyl- d -glutamate in the biosynthesis of bacterial cell-wall peptidoglycan synthesis [ 36 ]. It is experimentally validated as potential drug target of E.coli [ 37 ], Clostridium botulinum [ 9 ], Mycobacterium tuberculosis [ 38 ] etc. In this docked complex, G203, S232, R277 and A280 binds with C-3 position of hydroxyl group in benzene ring and nucleosyl group of Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine. S281 forms hydrogen bond with methoxy group at C3 & C9 position of selected drug compound. Alanine racemase is considered an excellent antibacterial drug target in several gram-positive pathogens, for example, Streptococcus sp. [ 39 ], Klebsiella sp. [ 40 ], Mycobacterium sp. [ 41 ] which is involved in catalysing the conversion of l-alanine to d-alanine utilised by bacterial cell wall for peptidoglycan synthesis. In this docked complex, 3-Hydroxy-Methyl group of {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid forms hydrogen bonds with V304, Q313, M323, P287 and N329. Vander Waals interactions with T347, I331 and R312 with pyridine group are important structural requirement for inhibitory activity. UDP-N-acetylglucosamine 1-carboxyvinyltransferase is an enzyme that catalyses the biosynthesis of peptidoglycans in the cell wall by adding enolpyruvyl to UDP-N-acetylglucosamine via an addition and elimination process in Mycobacterium tuberculosis [ 42 ], Wolbachia [ 43 ], Escherichia coli [ 44 ] etc. Major hydrogen bonding interaction with benzene ring (C-9th position of hydroxyl group and the phenyl ring D) could interact with the (oxygen atom) main chain of Q297, P300, Q311 and D321. V214 and R224 are found to be the major active residues for the inhibition of Uridine-Diphosphate-N-Acetylglucosamine. 5. Conclusion Oral microbes are responsible for several dental and periodontal diseases. The oral microbiome acts as a bridge between host and environment by regulating complex signaling and digestion processes. This study aims to explore the proteomics dataset of Olsenella uli DSM 7084 for plausible drug target identification that can be targeted in future for drug development. This ‘top-bottom’ systems biology approach includes non-homology analysis to avoid any possibility of cross reactivity with the host. Druggability analysis results in the mining of FDA approved drugs for respective prioritized drug targets, resulting in providing effective treatment for infections caused by Olsenella uli DSM 7084. Further, molecular docking approaches validates the findings of this study for effective treatment. Declarations Conflict of Interest Not applicable Author Contribution 1. MSA and TB conceived the idea.2. AT,AA and TB retrieved the data from NCBI and arranged the same in FASTA format3. MSA, AAA and TB performed subtractive screening and molecular docking. 4. MA,IAA, and SBG prepared the manuscript and cross-checked by MSA, TB and MMA5. 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J Enzyme Inhib Med Chem 31(4):517–526 Anthony KG, Strych U, Yeung KR, Shoen CS, Perez O, Krause KL, Cynamon MH, Aristoff PA, Koski RA (2011) New classes of alanine racemase inhibitors identified by high-throughput screening show antimicrobial activity against Mycobacterium tuberculosis. PLoS ONE 6(5):e20374 Isa MA (2019) Homology modeling and molecular dynamic simulation of UDP-N-acetylmuramoyl-l-alanine-d-glutamate ligase (MurD) from Mycobacterium tuberculosis H37Rv using in silico approach. Comput Biol Chem 78:116–126 Shahab M, Verma M, Pathak M, Mitra K, Misra-Bhattacharya S, Cloning (2014) Expression and Characterization of UDP-N-Acetylglucosamine Enolpyruvyl Transferase (MurA) from Wolbachia Endosymbiont of Human Lymphatic Filarial Parasite Brugia malayi . PLoS ONE 9(6):e99884 Zhu JY, Yang Y, Han H, Betzi S, Olesen SH, Marsilio F, Schönbrunn E (2012) Functional consequence of covalent reaction of phosphoenolpyruvate with UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA). J Biol Chem. 2012;287(16):12657-67 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileolsenella1.docx Supplementary File Table 1 List of metabolic pathways unique to Olsenella uli DSM 7084 Table 2 List of strain specific proteome dataset of Olsenella uli DSM 7084 Table 3 List of non-homologous essential proteins of Olsenella uli DSM 7084 Table 4 Physicochemical characterization of the non-homologous essential proteins of Olsenella uli DSM 7084 Table 5 Identified potential drugs after druggability analysis of 8 stable non-homologous essential proteins of Olsenella uli DSM 7084 Table 6 Prediction of subcellular localization of 8 stable non-homologous essential proteins of Olsenella uli DSM 7084 Table 7 List of PubChem ID for potential drugs from DrugBank for screened proteins and binding energies respectively utilizing AutoDock v 4.2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4456653","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305246579,"identity":"41070fd8-9c2f-40d0-9e81-8a0cbfd89671","order_by":0,"name":"Mohammad Salman Akhtar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFCCBAaGByCahwdE2jAwSBCjJQGihbGBgSGNdC2HCWvRbU9/+CExxy6Pv+fs8Qc/95xP7J/dfPABQ41NNC4tZmfeGEskbksuljjbl9jY8+x24ow7x5INGI6l5Tbg0nIjhwGohTmx4TyPYQPPgduJDTdyzCQYGw7j0ZL++EfitvrE+UAtjX8OnEucT1hLghnQlsOJG872GDbzHDiQuIGgljNvzCwStx1P3HjmjOFsmQPJxhtvpCUbJODzy/H0xzc+bqtOnHcmx+DjmwN2svNuJB988KHGBqcWDOAIVplArHIQsCdF8SgYBaNgFIwMAADPFWnl4R3UXwAAAABJRU5ErkJggg==","orcid":"","institution":"Al-Baha University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Salman","lastName":"Akhtar","suffix":""},{"id":305246580,"identity":"a8449f69-0660-4335-922a-6898c6a5a0dc","order_by":1,"name":"Arshi Talat","email":"","orcid":"","institution":"ITS Dental College, Hospital and Research, Greater Noida","correspondingAuthor":false,"prefix":"","firstName":"Arshi","middleName":"","lastName":"Talat","suffix":""},{"id":305246581,"identity":"bd266cf3-180e-4184-9b41-6bf6fe25a3ea","order_by":2,"name":"Tulika Bhardwaj","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Tulika","middleName":"","lastName":"Bhardwaj","suffix":""},{"id":305246582,"identity":"5e4ba3b3-95b7-4213-bfbc-b4b6e56a7998","order_by":3,"name":"Mansoor Alsahag","email":"","orcid":"","institution":"Al-Baha University","correspondingAuthor":false,"prefix":"","firstName":"Mansoor","middleName":"","lastName":"Alsahag","suffix":""},{"id":305246583,"identity":"726d210e-3cdf-4a06-b66a-1d56935c5194","order_by":4,"name":"Saleh Bakheet Al-Ghamdi","email":"","orcid":"","institution":"Al-Baha University","correspondingAuthor":false,"prefix":"","firstName":"Saleh","middleName":"Bakheet","lastName":"Al-Ghamdi","suffix":""},{"id":305246584,"identity":"505b438f-5f16-43ed-be5e-2e372deb9466","order_by":5,"name":"Aftab Ahmad","email":"","orcid":"","institution":"The Applied College, King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Aftab","middleName":"","lastName":"Ahmad","suffix":""},{"id":305246585,"identity":"fb466734-51f1-4588-b1f2-6fadc5a8c380","order_by":6,"name":"Anwar A. Alghamdi","email":"","orcid":"","institution":"The Applied College, King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Anwar","middleName":"A.","lastName":"Alghamdi","suffix":""},{"id":305246586,"identity":"fb7922b6-dc6c-4387-9a42-80c9a80611cf","order_by":7,"name":"Ibrahim A Alotibi","email":"","orcid":"","institution":"The Applied College, King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"A","lastName":"Alotibi","suffix":""},{"id":305246587,"identity":"7981dc7d-ebd6-496d-9e6e-8c9d319f485a","order_by":8,"name":"Md. Margoob Ahmad","email":"","orcid":"","institution":"Indira Gandhi Institute of Medical Sciences (IGIMS)","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Margoob","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2024-05-21 18:45:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4456653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4456653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57593452,"identity":"162bc844-a36a-4c7e-8ed9-8325d3c9a154","added_by":"auto","created_at":"2024-06-03 06:14:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1069984,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of docked (a) Complex 1: UDP-N-acetylmuramoylalanine--D-glutamate ligase-\u003ca href=\"https://go.drugbank.com/drugs/DB01673\"\u003eUridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine\u003c/a\u003e, (b) Complex 2 alanine racemase-\u003ca href=\"https://go.drugbank.com/drugs/DB03327\"\u003e{1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid\u003c/a\u003e\u0026nbsp; and (c) Complex 3: UDP-N-acetylglucosamine-1-carboxyvinyltransferase-\u003ca href=\"https://go.drugbank.com/drugs/DB03397\"\u003eUridine-Diphosphate-N-Acetylglucosamine\u003c/a\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4456653/v1/65442968bfd252bb8968e8fe.jpg"},{"id":68828028,"identity":"3474d036-ad12-4a93-8ded-a61d05c65ac8","added_by":"auto","created_at":"2024-11-12 12:24:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1738822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4456653/v1/00e48b63-7af5-49fc-ba90-3dcddae4312b.pdf"},{"id":57593469,"identity":"c4dd6302-8dbc-460d-8d56-df5e0773da3c","added_by":"auto","created_at":"2024-06-03 06:14:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50588,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File\u0026nbsp;\u003cbr\u003e\nTable 1\u003cbr\u003e\nList of metabolic pathways unique to \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084\u003cbr\u003e\nTable 2\u003cbr\u003e\nList of strain specific proteome dataset of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084\u003cbr\u003e\nTable 3\u003cbr\u003e\nList of non-homologous essential proteins of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084\u003cbr\u003e\nTable 4\u003cbr\u003e\nPhysicochemical characterization of the non-homologous essential proteins of \u003cem\u003eOlsenella uli\u0026nbsp;\u003c/em\u003eDSM 7084\u003cbr\u003e\nTable 5\u003cbr\u003e\nIdentified potential drugs after druggability analysis of 8 stable non-homologous essential proteins of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084\u003cbr\u003e\nTable 6\u003cbr\u003e\nPrediction of subcellular localization of\u0026nbsp;8 stable\u0026nbsp;non-homologous essential proteins of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084\u003cbr\u003e\nTable 7\u003cbr\u003e\nList of PubChem ID for potential drugs from DrugBank for screened proteins and binding energies respectively utilizing AutoDock v 4.2\u003c/p\u003e","description":"","filename":"SupplementaryFileolsenella1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4456653/v1/792cb24ec2e6492f1e9826fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteomics based systematic exploration of the peptidoglycan biosynthesis of Olsenella uli DSM 7084 towards pathogenesis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOral diseases such as tooth decay, periapical periodontitis, and endodontics have evolved into serious global public health problems. Environmental, biological, and lifestyle-related risk factors all contribute to dental caries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In terms of biological aspects, oral microflora found in the crowns and roots of teeth play a significant influence in oral health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mutans \u003cem\u003estreptococci, Porphyromonas, Olsenella sp. and Prevotella intermedia\u003c/em\u003e particularly \u003cem\u003eStreptococcus mutans (S. mutans), Olsenella uli\u003c/em\u003e and \u003cem\u003eStreptococcus sobrinus\u003c/em\u003e, are the main cause of coronal and root caries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Some acid-tolerant strains, including \u003cem\u003eStreptococcus sanguis and Streptococcus gordonii\u003c/em\u003e are also suspected of being responsible for coronal caries. \u003cem\u003eOlsenella uli is\u003c/em\u003e one out of thirteen genera in the family \u003cem\u003eCoriobacteriaceae\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In root canal samples collected after chemo-mechanical preparation and intracanal medication, \u003cem\u003eO. uli\u003c/em\u003e was discovered to predominate over other Gram-positive rods, such as \u003cem\u003eAtopobium parvulum\u003c/em\u003e, indicating that this species can resist intracanal disinfection procedures and may be responsible for persistent infections. It is a gram-positive bacterium from the genus \u003cem\u003eOlsenella\u003c/em\u003e which was isolated from the gingival cervices and periodontal pockets of human [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eOlsenella uli DSM 7084\u003c/em\u003e (NCBI Accession Number:NC_014363.1) is a double stranded genome of 2.05 Mb size and 64.7 GC% consisting of 1742 proteins, 1807 genes, 49 tRNA and 3 rRNA.\u003c/p\u003e \u003cp\u003eThe traditional drug discovery process takes approximately ten to fifteen years to introduce a drug to market in the traditional drug discovery system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Drug target identification in the bench-top methods is time-consuming and expensive. As a result, drug target identification using a computational-based bioinformatics approach is the most preferable method because it is time and money efficient [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Owing to the availability of omics data, systematic identification of prioritized drug targets is easily achieved. Several studies indicate the potential of subtractive proteome screening towards the identification of drug targets for \u003cem\u003eClostridium botulinum\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], \u003cem\u003eKlebsiella\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], \u003cem\u003eHelicobacter pylori\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] etc. Protein-protein network analysis aids in the identification and characterization of promising drug targets of virulent uropathogen \u003cem\u003eEscherichia coli\u003c/em\u003e strain CFT073 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Metabolic pathways are considered attractive targets for drug target identification as they constitute the core proteome of genome which is indispensable for cell survival. Targeting such proteome results in the eradication of infection caused by the organism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, in present study, metabolic pathways of host and pathogen \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 are targeted to identify potential pathogen specific proteins. Further, subtractive proteome screening via druggability analysis, sub cellular localization, physio-chemical characterization enables the identification of potential drug targets of pathogen and FDA approved drugs to be utilized in future to treat the infections caused by query pathogen.\u003c/p\u003e"},{"header":"2. Materials and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Primary dataset collection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eComparative genome analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe publically available genomes of the \u003cem\u003eOlsenella\u003c/em\u003e species were obtained in fasta and GenBank format from genome database of NCBI (Table). EDGAR 3.0, a user-friendly platform enabling genome-level comparison and evolutionary tracking among sequenced strains was utilized for pangenome composition. The core genome is computed along with phy\u003c/p\u003e \u003cp\u003eThe proteome information about the metabolic pathways of pathogen \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 and host \u003cem\u003eHomo sapiens\u003c/em\u003e were iterated from Kyoto Encyclopedia of Genes and Genomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is knowledge database for systematic analysis of gene functions, genomic information with functional information. The comparative analysis was manually performed to identify the strain specific metabolic pathway present in the pathogen only. The corresponding protein sequences of enzymes involved in unique (strain specific) pathways were iterated from KEGG database in FASTA format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Non-homologous essential protein selection\u003c/h2\u003e \u003cp\u003eThe non-homology analysis of proteome dataset from unique metabolic pathways was completed in two phases. In phase I, sequence similarity search was performed for mined dataset against host (\u003cem\u003eHomo sapiens\u003c/em\u003e) proteome utilizing BLASTP based on a threshold expectation value (e-value) of 0.005, matching similarity of \u0026le;\u0026thinsp;35%, and minimum bit score of 100. In phase II, screened non-homologous protein sequences from Phase I were subjected to essentiality analysis against DEG microbial BLASTP with a cutoff e-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and a least possible bit score of 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Physio-chemical characterization of non-homologous essential proteins\u003c/h2\u003e \u003cp\u003ePhysio-chemical characterization enables the better understanding of aliphatic index, thickness and stability of drug target. ProtParam [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] is a tool which allows the computation of physical and chemical parameters for a given protein sequence \u003cem\u003ei.e.\u003c/em\u003e isoelectric point, molecular weight, number of positively and negatively charged residues, instability index, aliphatic index, extinction coefficient, GRAVY (Grand average of hydropathicity). The instability index provides an estimate of the stability of your protein in a test tube. The aliphatic index is an important factor for analysing the thermostability of globular proteins by defining the relative volume occupied by aliphatic side chains (alanine, valine, isoleucine, and leucine). It is computed by formula:\u003c/p\u003e \u003cp\u003eAliphatic Index\u0026thinsp;=\u0026thinsp;X(Ala)\u0026thinsp;+\u0026thinsp;a * X(Val)\u0026thinsp;+\u0026thinsp;b * (X(Ile)\u0026thinsp;+\u0026thinsp;X(Leu))\u003c/p\u003e \u003cp\u003ewhere, X(Ala), X(Val), X(Ile) and X (Leu) are mole percent of Alanine, valine, Isoleucine and Leucine\u003c/p\u003e \u003cp\u003eThe coefficients of a and b\u0026thinsp;=\u0026thinsp;relative volume of valine side chains and Leu/Ile to the side chains of alanine (a\u0026thinsp;=\u0026thinsp;2.9 and b\u0026thinsp;=\u0026thinsp;3.9)\u003c/p\u003e \u003cp\u003eThe ratio of sum of hydropathy values of all amino acids by the number of residues in the sequence is defined as Grand average of hydropathicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Druggability analysis of stable non-homologous essential proteins\u003c/h2\u003e \u003cp\u003eThe druggability of the identified essential proteins was assessed using DrugBank (ver. 4.3) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which provides unique bioinformatic and cheminformatic data on drugs and drug targets. The FDA-approved small molecule drugs, biotech (protein/peptide) drugs, nutraceuticals, and experimental drugs were among the drug entries that were used to align the proteins using the default parameters. The resultant hits found with the DrugBank database were defined as druggable targets, whereas the non-hits were considered unique targets and needed to be evaluated experimentally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sub-cellular localisation of druggable targets\u003c/h2\u003e \u003cp\u003eProtein's sub-cellular localization is a critical step in identifying it as a potential therapeutic target. This leads to a better understanding of each protein's function, which aids in distinguishing between targets for antimicrobial agents and those for vaccine therapy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. CELLO v.2.5 (multi-class support vector machine classification system) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] predicts the biological significance and subcellular localization of the proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Three-dimensional protein structure generation\u003c/h2\u003e \u003cp\u003eThe protein sequences of all screened drug targets were mined from KEGG database in FASTA format. Further, I-TASSER was utilized to generate three-dimensional structure of all screened target protein sequences. The iterative threading assembly refinement (I-TASSER) is a unified platform for automated structure generation and functional prediction utilizing multiple threading algorithms and iterative structural assembly simulations. Based on pair-wise structural similarity, C-score was generated for each iteration. C-score is computed based significance of threading template alignments and the convergence parameters of the structure assembly simulations which ranges between \u0026minus;\u0026thinsp;5 and 2. Further, high C-score signifies a model of high confidence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular Docking\u003c/h2\u003e \u003cp\u003eAn automated flexible docking suite, AutoDock v 4.2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was utilized for performing \u003cem\u003ein-silico\u003c/em\u003e molecular docking. Drugs iterated from DrugBank in SDF format for the concerned drug target were utilized for docking. PDB files were converted into PDBQT files by assigning Kollman charges and hydrogen atoms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A grid of 70 x 70x 70 for x, y and z coordinates, respectively were taken and centered at the binding pockets identified by PockDrug software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To explore the optimized conformational, positional thresholds and maximum possible orientations, a total number of 30 runs were considered to compute the binding energies using Lamarckian genetic algorithm at default parameters [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Binding energies were computed based on the adaptive local search global optimization algorithm. PyMol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org\u003c/span\u003e\u003cspan address=\"https://pymol.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) enables the visualization of docked complexes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Dataset collection\u003c/h2\u003e \u003cp\u003eThe information about metabolic pathways of pathogen and host was mined from the KEEG database This includes KEDD ID of metabolic pathways, pathway name for both the genomic organism. Manual curation of comparative analysis results in the identification of 21 unique (strain-specific) pathways \u003cb\u003e(Supplementary File\u003c/b\u003e: Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The protein sequences of all the genes participating in unique pathways were mined from NCBI in FASTA format \u003cb\u003e(Supplementary File\u003c/b\u003e: Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Non-homologous essential protein selection\u003c/h2\u003e \u003cp\u003eBLASTp enables the similarity search analysis of screened proteome dataset of unique pathways against the Database of Essential Genes (DEG) which is a collection of important genes that can be discovered in a variety of pathogenic and non-pathogenic species (both pro- and eukaryotes). A total of 14 proteins were identified as essential proteins required for the viability of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 and subjected to further screening prior considered as potent drug targets (\u003cb\u003eSupplementary File: Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 Physio-chemical characterization of non-homologous essential proteins\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003cem\u003eIn-silico\u003c/em\u003e physio chemical characterisation enables the computation of molecular weight, aliphatic index, instability index and grand average value of hydropathicity. The computation of an amino acid sequence's isoelectric point (theoretical pI) and molecular weight (Mw) is beneficial because these data dictate the approximate area of a 2D-gel where a protein of interest may be detected. Most proteins are acidic or nearly neutral in nature with isoelectric point ranging from (4.66 to 6.58). with Aliphatic index define the thermal stability of proteins. Aliphatic amino acids are hydrophobic on nature The aliphatic index of screened non-homologous essential proteins in the range of 70 to 102.64 indicates that these proteins are thermally stable as well as they contain high amount of hydrophobic amino acids. It results in the amphipathic nature of proteins due to the copresence of hydrophobic and polar (charged) residues [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Instability index shows that among 14 screened targets, 8 are stable in nature (instability index\u0026thinsp;\u0026lt;\u0026thinsp;40). Therefore, in this study only stable targets are considered for further subtractive screening of drug compounds (\u003cb\u003eSupplementary File: Table\u0026nbsp;4).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Druggability analysis\u003c/h2\u003e \u003cp\u003eThe druggability analysis of a protein is an important step in drug target identification that was assessed based on the assumption that druggable protein targets should interact with the drug-like compound. Therefore, the DrugBank database was used to identify the drug targets. Eight essential non-homologous stable proteins that were found in the prediction of physio-chemical characterization step were subjected to the DrugBank database against BLASTp search with default settings. DrugBank hits were classified as common targets or druggable targets, while the remaining hits were categorised as novel drug targets that are further encouraged for experimental validation \u003cb\u003e(Supplementary File: Table\u0026nbsp;5).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subcellular localisation of druggable targets\u003c/h2\u003e \u003cp\u003eFor identification of the biological significance of its function and subcellular localization, all predicted non-homologous druggable stable were subjected to subcellular localization prediction. The resultant output from CELLO v.2.5 reveals that majority of the screened proteins were present in the cytoplasm \u003cb\u003e(Supplementary File: Table\u0026nbsp;6).\u003c/b\u003e The proteins that reside in Cytoplasmic might be an attractive drug target [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.6 Three-dimensional protein structure generation\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe homology modelling of possible drug targets was carried out by performing I-TASSER, a commonly used and precise computer program for predicting the three-dimensional structure of the \u0026lsquo;target\u0026rsquo; protein. Models generated from I-TASSER have already undergone quality assessment and predicts the final optimal model by means of C-score. The resultant C-score of selected models for eight screened proteins are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eEnlisting C-score of best optimal model generated for screened 8 potential drug targets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrug Targets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-score\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\u003eS-ribosylhomocysteine lyase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/b\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\u003eUDP-N-acetylglucosamine\u0026ndash;N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\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\u003ealanine racemase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026thinsp;\u003cb\u003e\u0026plusmn;\u0026thinsp;0.11\u003c/b\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\u003eUDP-N-acetylmuramoylalanine\u0026ndash;D-glutamate ligase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/b\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\u003eUDP-N-acetylglucosamine 1-carboxyvinyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\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\u003e4-alpha-glucanotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\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\u003ebeta-glucosidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026thinsp;\u003cb\u003e\u0026plusmn;\u0026thinsp;0.11\u003c/b\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\u003easpartate-semialdehyde dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\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=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Molecular Docking\u003c/h2\u003e \u003cp\u003eThe intermolecular interactions among proteins and ligands were analyzed for computing binding energies of protein-ligand complexes using AutoDock v 4.2 (\u003cb\u003eSupplementary File: Table\u0026nbsp;7)\u003c/b\u003e. Three docked complexes with minimum binding energy were selected for detailed analysis in this study (Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine, {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid and Uridine-Diphosphate-N-Acetylglucosamine for UDP-N-acetylmuramoylalanine\u0026ndash;D-glutamate ligase, alanine racemase and UDP-N-acetylglucosamine-1-carboxyvinyltransferase respectively).PubChem database was mined for the retrieval of chemical structures of the prioritized drugs. The binding interactions of docked complexes are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Uridine-Diphosphate-N-Acetylglucosamine was found to be most effective in inhibiting UDP-N-acetylglucosamine-1-carboxyvinyltransferase with a binding energy of -9.66 kcal/mol followed by Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine for UDP-N-acetylmuramoylalanine\u0026ndash;D-glutamate ligase with \u0026minus;\u0026thinsp;9.16 kcal/mol and {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid for alanine racemase with \u0026minus;\u0026thinsp;8.76 kcal/mol binding energy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eComputed binding energies of the docked complexes\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=\"char\" char=\".\" 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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDocked energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInter molecular energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTorsional energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInternal energy (kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.99\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"},{"header":"4. Discussion","content":"\u003cp\u003eBacteremia is well documented following dental procedures such as tooth extraction, endodontic treatment, periodontal surgery, and root scaling [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The protective function of the oral epithelium over the underlying tissues, the antibacterial properties of saliva, and the immune responses of the phagocytes all contribute to the rarity of primary bacterial infections of the oral mucosa [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The risk of primary bacterial infections increases if the oral mucosa is damaged by poor oral hygiene, trauma, smoking and alcohol abuse. Major bacterial agents responsible for endodontic infections are \u003cem\u003eActinobacillus actinomycetemcomitans, Porphyromonas gingivalis, Olsenella uli\u003c/em\u003e and \u003cem\u003ePrevotella intermedia.\u003c/em\u003e Typically found in the mouth or gastrointestinal tract, \u003cem\u003eOlsenella uli\u003c/em\u003e is an anaerobic or microaerophilic bacteria. In the human mouth, lesions such as gingival and subgingival sites with periodontitis are most often places from where \u003cem\u003eOlsenella uli\u003c/em\u003e is isolated [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 genome has been found to have a number of distinct metabolic pathways that do not exist in their natural host. These distinct pathways make it possible to find antimicrobials that target the pathogen specifically and are therefore safe. Furthermore, non homology essentiality analysis enables the identification of non-homologous genome content against host proteome which is indispensable for pathogen survival. Non-homology analysis diminishes the changes of any drug side-effect and cross-reactivity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Designing effective therapeutic agents with the objective of inhibiting the pathogen's survival and/or replication has an added bonus when essential genes and proteins in particular metabolic pathways are targeted [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A protein's cellular localization must be identified before it can be considered a potential target for therapeutic intervention to clearly differentiate between targets for antimicrobial agents and those for vaccine therapy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In support, physio-chemical characterization provides the platform for future bench-top experiments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, the ability of FDA-approved, nutraceutical, or experimental drugs to bind proteins similar to the identified pathogen essential proteins shows that these proteins may be druggable as therapeutic targets [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and opens the door to the use of various drug combinations to treat \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 infections in human.\u003c/p\u003e \u003cp\u003eThe present subtractive proteome screening of strain specific pathways of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 renders 8 potential drug targets (Table\u0026nbsp;4). These screened targets are considered indispensable for pathogen survival therefore serve as precursor for drug discovery and vaccine development. In this study, 8 potential drug targets were subjected to molecular docking with their screened FDA approved drugs to identify top three docked complexes with minimum binding energy i.e. (Complex 1)UDP-N-acetylmuramoylalanine\u0026ndash;D-glutamate ligase-Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine, (Complex 2) alanine racemase-{1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid and (Complex 3) UDP-N-acetylglucosamine-1-carboxyvinyltransferase-Uridine-Diphosphate-N-Acetylglucosamine of -9.16 kcal/mol,-8.76 kcal/mol and \u0026minus;\u0026thinsp;9.66 kcal/mol binding energies respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). UDP-N-acetylmuramoylalanine\u0026ndash;D-glutamate ligase is a cytoplasmic enzyme involved in the catalytic addition of \u003cem\u003emeso\u003c/em\u003e-diaminopimelic acid to nucleotide precursor UDP-\u003cem\u003eN\u003c/em\u003e-acetylmuramoyl-\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003el\u003c/span\u003e-alanyl-\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ed\u003c/span\u003e-glutamate in the biosynthesis of bacterial cell-wall peptidoglycan synthesis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is experimentally validated as potential drug target of \u003cem\u003eE.coli\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], \u003cem\u003eClostridium botulinum\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] etc. In this docked complex, G203, S232, R277 and A280 binds with C-3 position of hydroxyl group in benzene ring and nucleosyl group of Uridine-5'-Diphosphate-N-Acetylmuramoyl-L-Alanine. S281 forms hydrogen bond with methoxy group at C3 \u0026amp; C9 position of selected drug compound.\u003c/p\u003e \u003cp\u003eAlanine racemase is considered an excellent antibacterial drug target in several gram-positive pathogens, for example, \u003cem\u003eStreptococcus\u003c/em\u003e sp. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], \u003cem\u003eKlebsiella\u003c/em\u003e sp. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], \u003cem\u003eMycobacterium\u003c/em\u003e sp. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] which is involved in catalysing the conversion of l-alanine to d-alanine utilised by bacterial cell wall for peptidoglycan synthesis. In this docked complex, 3-Hydroxy-Methyl group of {1-[(3-Hydroxy-Methyl-5-Phosphonooxy-Methyl-Pyridin-4-Ylmethyl)-Amino]-Ethyl}-Phosphonic Acid forms hydrogen bonds with V304, Q313, M323, P287 and N329. Vander Waals interactions with T347, I331 and R312 with pyridine group are important structural requirement for inhibitory activity. UDP-N-acetylglucosamine 1-carboxyvinyltransferase is an enzyme that catalyses the biosynthesis of peptidoglycans in the cell wall by adding enolpyruvyl to UDP-N-acetylglucosamine via an addition and elimination process in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], \u003cem\u003eWolbachia\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], \u003cem\u003eEscherichia coli\u003c/em\u003e [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] etc. Major hydrogen bonding interaction with benzene ring (C-9th position of hydroxyl group and the phenyl ring D) could interact with the (oxygen atom) main chain of Q297, P300, Q311 and D321. V214 and R224 are found to be the major active residues for the inhibition of Uridine-Diphosphate-N-Acetylglucosamine.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOral microbes are responsible for several dental and periodontal diseases. The oral microbiome acts as a bridge between host and environment by regulating complex signaling and digestion processes. This study aims to explore the proteomics dataset of \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084 for plausible drug target identification that can be targeted in future for drug development. This \u0026lsquo;top-bottom\u0026rsquo; systems biology approach includes non-homology analysis to avoid any possibility of cross reactivity with the host. Druggability analysis results in the mining of FDA approved drugs for respective prioritized drug targets, resulting in providing effective treatment for infections caused by \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084. Further, molecular docking approaches validates the findings of this study for effective treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. MSA and TB conceived the idea.2. AT,AA and TB retrieved the data from NCBI and arranged the same in FASTA format3. MSA, AAA and TB performed subtractive screening and molecular docking. 4. MA,IAA, and SBG prepared the manuscript and cross-checked by MSA, TB and MMA5. All authors have seen the manuscript and agree with its submission.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research work was funded by Institutional Fund Projects under grant no. (IFPIP 1330-156-1443). The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKashyap B, Mikkonen JJW, Bhardwaj T, Dekker H, Schulten EAJM, Bloemena E, Kullaa AM (2022) Effect of smoking on MUC1 expression in oral epithelial dysplasia, oral cancer, and irradiated oral epithelium. Arch Oral Biol 142:105525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFigdor D (2004) Microbial aetiology of endodontic treatment failure and pathogenic properties of selected species. Aust Endod J 30:11\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiqueira JF (2002) Jr. Endodontic infections: concepts, paradigms, and perspectives. 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PLoS ONE 9(6):e99884\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu JY, Yang Y, Han H, Betzi S, Olesen SH, Marsilio F, Sch\u0026ouml;nbrunn E (2012) Functional consequence of covalent reaction of phosphoenolpyruvate with UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA). \u003cem\u003eJ Biol Chem.\u003c/em\u003e 2012;287(16):12657-67\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Oral infections, Olsenella uli, subtractive proteomics, druggability analysis, physio-chemical characterization, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-4456653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4456653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe oral microbiota plays a pivotal role in either promoting health or exacerbating disease progression. Within the diverse microbial community, \u003cem\u003eOlsenella uli\u003c/em\u003e emerges as a concerning pathogen linked to various endodontic infections. Advancements in next-generation sequencing methods and bioinformatics have begun unraveling the intricate nature of the oral microbiome. Understanding these oral microorganisms opens doors to exploring functional and metabolic changes, offering valuable insights for drug development and targeted therapies. Consequently, our current investigation employs a comparative subtractive proteomics approach to identify potential drug targets within \u003cem\u003eOlsenella uli\u003c/em\u003e DSM 7084. This effort unveils eight promising drug target candidates, which undergo thorough assessment for druggability and sub-cellular localization. Furthermore, molecular docking simulations involving these prioritized targets and FDA-approved drugs establish a foundational framework for future researchers, expediting the drug development process aimed at combating infections caused by this formidable pathogen. Our research intends to accomplish precision drug target discovery using an integrated method that integrates subtractive proteome analysis, systems biology, and molecular docking. This method paves the path for more precise molecular docking investigations by enabling a thorough understanding of prospective pharmacological targets.\u003c/p\u003e","manuscriptTitle":"Proteomics based systematic exploration of the peptidoglycan biosynthesis of Olsenella uli DSM 7084 towards pathogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 06:14:11","doi":"10.21203/rs.3.rs-4456653/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":"c448c70e-dcf7-48ac-af35-0a56db2d56f0","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-12T12:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-03 06:14:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4456653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4456653","identity":"rs-4456653","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}