In Silico Functional Annotation of Hypothetical Proteins from Burkholderia pseudomallei strain GTC3P0254T

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Saiful, Sumaya Afroz, Jannatul Ferdaous This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6292329/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 Burkholderia pseudomallei , the causative agent of melioidosis, contains numerous hypothetical proteins (HPs) with unknown functions, limiting our understanding of its biology and pathogenicity. This study employed in silico approaches to functionally annotate 27 HPs from B. pseudomallei strain GTC3P0254T using domain analysis, physicochemical characterization, structural modeling, and protein-protein interaction predictions. The identified HPs were classified into enzymes, transporters, binding proteins, regulatory proteins, and structural proteins. Notably, several HPs exhibited enzymatic activity, including polyphosphate kinase and isoprenoid synthase, which play crucial roles in bacterial metabolism and survival. Additionally, membrane-associated proteins were linked to drug resistance and host adaptation, while one HP demonstrated ubiquitin hydrolase activity, a function associated with bacterial invasion and virulence. Homology-based tertiary structure predictions were validated using multiple structural assessment tools, and protein-protein interaction analyses provided insights into their functional associations. These findings enhance our understanding of B. pseudomallei pathogenesis and antimicrobial resistance, highlighting potential targets for therapeutic interventions. However, since this study is based solely on computational predictions, experimental validation through biochemical assays and genetic studies is essential to confirm these findings. Future research should explore these HPs as potential drug targets and diagnostic biomarkers to improve treatment strategies for melioidosis. Bioinformatics Hypothetical proteins (HPs) Domain Motif Tertiary structure Bioinformatics Pathogenesis Genomics and proteomics Computational biology Melioidosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Burkholderia pseudomallei is a Gram-negative, aerobic, soil-dwelling bacterium responsible for melioidosis, a disease predominantly found in Southeast Asia and Northern Australia [ 1 ]. The disease has a mortality rate of 20–50% [ 2 ]. B. pseudomallei thrives at an optimal temperature of 37°C and a pH range of 6.5–7.5 [ 3 ]. Due to its high mortality rate, heat resistance, acid tolerance, and airborne transmission capability, the Centers for Disease Control and Prevention (CDC) has classified it as a Tier 1 Select Agent [ 4 ]. The bacterium can also infect animals, particularly livestock such as sheep, pigs, and goats [ 5 ]. It is inherently resistant to gentamicin [ 6 ] and colistin, aiding its identification in clinical settings [ 7 ]. Although kanamycin is used in laboratory settings to eliminate B. pseudomallei , the concentrations required far exceed what is feasible in humans [ 8 ]. Several vaccine candidates have been assessed in preclinical research, but as of 2023, none have been licensed [ 9 , 10 ]. The genome of B. pseudomallei strain GTC3P0254T has been fully sequenced, consisting of two chromosomes of 3.93 Mb and 3.14 Mb, respectively [ 11 ]. According to the National Center for Biotechnology Information (NCBI) database, the genome encodes 6,077 protein-coding genes. However, approximately 16% of these are predicted to encode proteins for which no experimental expression or functional data are available—referred to as hypothetical proteins (HPs). Given their unknown roles, functional annotation of these proteins is essential to determine their physicochemical properties, subcellular localization, tertiary structures, ligand-binding sites, sequence alignments, phylogenetic relationships, and motif structures using various bioinformatics tools. This study evaluated 86 HPs out of 974 identified in B. pseudomallei . Through functional annotation using InterPro [ 12 ], SMART [ 13 ], and the Conserved Domain Database (CDD) [ 14 , 15 ], we successfully predicted the functions of 27 HPs. These were classified as binding proteins (including DNA- and RNA-binding proteins, cofactors, and membrane-bound proteins), enzymes (involved in metabolism, biosynthesis, and survival), regulatory proteins, and one structural protein. A range of bioinformatics tools was utilized, including Expasy ProtParam [ 16 ] for physicochemical property prediction, PSORTb [ 17 ], CELLO [ 18 ], and TMHMM [ 19 ] for subcellular localization, and MEME [ 20 ] for motif analysis. Additionally, Clustal Omega [ 21 ] was used for multiple sequence alignment and phylogenetic analysis, while SWISS-MODEL [ 22 ] was employed for homology modeling and structure validation. By elucidating the possible functions of these HPs, this study provides a foundation for future research into their potential applications in clinical and biotechnological fields. 2. Materials and Methods 2.1 Sequence Retrieval The genome of B. pseudomallei strain GTC3P0254T comprises two chromosomes with a total length of 7.1 Mb [ 11 ], encoding 6,077 protein-coding genes. Among these, 974 were identified as hypothetical proteins. A subset of 86 HPs was selected for functional analysis, out of which 27 were successfully annotated based on domain identification using InterPro, SMART, and CDD. Proteins lacking conserved domains or motifs with sufficient evidence for biological role prediction (59 HPs) were excluded due to ambiguous annotations. The selected sequences were retrieved in FASTA format from the NCBI database. 2.2 Functional Annotation of Hypothetical Proteins Functional domains of selected HPs were predicted using InterPro [ 12 ], SMART [ 13 ], and the Conserved Domain Database (CDD) [ 14 , 15 ]. These tools enabled the identification of conserved protein motifs and domains, facilitating classification into functional groups. 2.3 Prediction of Physicochemical Properties The physicochemical properties of the 27 HPs were computed using Expasy’s ProtParam tool. Key parameters, including molecular weight, theoretical isoelectric point (pI), charge distribution, extinction coefficient [ 23 ], instability index [ 24 ], aliphatic index (AI), and grand average of hydropathicity (GRAVY) [ 25 ], were calculated to assess protein stability and solubility. 2.4 Prediction of the Subcellular Localization The subcellular localization of HPs was predicted using PSORTb [ 17 ], CELLO [ 18 ], and TMHMM [ 19 ]. PSORTb is an open-source tool for bacterial and archaeal localization prediction, while CELLO uses a support vector machine-based approach for cellular compartment prediction. TMHMM was used to identify transmembrane helices. 2.5 Prediction of Transmembrane Proteins TMHMM 2.0 [ 19 ] was used to predict transmembrane helices and distinguish membrane-bound from soluble proteins. 2.6 Predicting Tertiary Structure of the Protein Tertiary structures were modeled using SWISS-MODEL [ 22 ], a homology-based server. Templates with > 30% sequence identity and high-resolution X-ray structures were prioritized. 2.7 Quality Assessment Structural validation was performed using PROCHECK [ 26 ], ERRAT [ 27 ], VERIFY3D [ 28 , 29 ], QMEANDisCo [ 30 ], and QMEAN Z-scores [ 31 ]. Models with > 90% Ramachandran-favored residues and ERRAT scores > 90% were retained. 2.8 Prediction of Ligand Binding Sites The prediction of ligand binding sites were determined using COACH server [ 32 ]. The COACH consensus algorithm is among the most effective methods for predicting protein-ligand interaction sites. 2.9 Prediction of Protein Protein Interaction Protein-protein interaction (PPI) networks were analyzed using the STRING database [ 33 ], which integrates functional and physical interactions based on genomic context, co-expression, curated databases, and text mining. 2.10 Multiple Sequence Alignment and phylogenetic analysis Multiple sequence alignment and phylogenetic analyses were performed using Clustal Omega [ 21 ] to determine evolutionary relationships between HPs and homologous proteins. 2.11 Motif Analysis. Motif analysis was conducted using the MEME Suite v5.5.7 [ 20 ] to identify conserved sequence motifs across homologous proteins. 3. Results and Discussions Phase I and II 3.1 Functional Annotation of Hypothetical Proteins Domains are distinct functional and/or structural units in a protein. Usually, they are responsible for a particular function or interaction, contributing to the overall role of a protein. Here in the following table respective functions of 27 HPs are determined. Then classified on the basis of functional annotation using various online tools ( Fig. 2 ), functions are mentioned in Table S1. (Supplementary materials) a. Enzymatic Proteins Enzymes are biological molecules, usually proteins, that act as catalysts in biochemical reactions, meaning they speed up chemical reactions without being consumed in the process. Enzymes are essential for many biological functions, such as digestion, energy production, DNA replication, and more. Among the 27 hypothetical proteins, eight were predicted to have enzymatic functions. BEH40390.1 classified as Tagatose/fructose phosphokinase, it would be catalyzing the phosphorylation of tagatose and fructose [ 34 ]. BEH40255.1, this hypothetical protein had O-antigen ligase domain, involved in the assembly of O-antigens in lipopolysaccharides [ 35 ] and BEH35088.1 (Polyphosphate kinase activity) protein’s domain play role for synthesizing polyphosphate [ 36 – 37 ] BEH39008.1 protein was predicted to be have isoprenoid synthase domain that synthesizes isoprenoids, crucial for various biosynthetic pathways [ 38 ]. P-loop containing nucleoside triphosphate hydrolase domain expressed by BEH37235.1 hypothetical protein that hydrolyzes nucleoside triphosphates, involved in energy metabolism [ 39 ]. One HP BEH37114.1 was classified as NAD(P)-binding domain superfamily that utilize NAD(P) as a cofactor in various metabolic reactions [ 40 – 41 ]. BEH37677.1 (CDP-glycerol:poly(glycerophosphate) glycerophosphotransferase TagF) is essential for wall teichoic acid polymerization, catalyzing the sequential transfer of glycerol-phosphate units from CDP-glycerol to the linkage unit lipid [ 42 ]. It has been demonstrated that the TagF-like family of enzymes is in charge of wall teichoic acid priming and polymerization processes [ 43 – 44 ]. BEH35088.1, this protein has polyphosphate kinase activity. In M. tuberculosis, it is important for its survival in macrophages [ 45 ]. BEH39920.1 has ubiquitin carboxyl-terminal hydrolase activity and SbsA, Ig-like domain. The ubiquitin C-terminal hydrolase activity, which promotes the invasion of epithelial cells by Listeria monocytogenes and Salmonella enterica [ 46 ]. Here, we case studied of this protein considering its virulence and target for therapeutic drugs. b. Structural and Adhesion Proteins Structural and adhesion proteins are vital components in cells and tissues, playing crucial roles in providing structural integrity and mediating interactions between cells or between cells and the extracellular matrix (ECM). There were four structural and adhesion proteins. BEH40411.1 expressed Bacterial collagen-like proteins domain contributing to bacterial adhesion and cell integrity [ 47 ]. BEH39177.1 (Trimeric autotransporter adhesin YadA, Serralysin-like metalloprotease domain) protein that combines structural and enzymatic functions for adhesion and proteolytic activity [ 48 – 50 ]. BEH39837.1 was classified as YadA-like membrane anchor domain that was membrane-anchoring domain facilitating bacterial adhesion. Six-bladed beta-propeller, TolB-like structural domain expressed by BEH40106.1 often involved in protein-protein interactions [ 51 ]. c. DNA and RNA Processing Proteins DNA and RNA processing proteins are essential for managing the integrity, replication, and expression of genetic information within cells. These proteins are involved in several critical processes, such as DNA replication and repair, RNA transcription, splicing, editing, and stability. Five proteins of this type were identified. FtsK/SpoIIIE/SftA domain (BEH35991.1) involved in bacterial cell division and DNA segregation [ 52 ]. BEH39282.1 classified as Endonuclease GajA/Old nuclease/RecF-like, AAA domain, ATPase, AAA-type, core which was involved in DNA repair and processing [ 53 ]. Restriction endonuclease type II-like, tRNA endonuclease-like domain superfamily was expressed by BEH39277.1 that cleave DNA and RNA, critical for restriction-modification systems [ 54 ]. BEH38760.1 (UvrD-like helicase C-terminal domain) involved in DNA repair. Relaxes domain of MobM and similar proteins expressed by BEH38753.1 which Facilitates DNA relaxation during plasmid transfer [ 55 – 56 ] d. Transport and Secretion Proteins Five proteins out of 27 were transport and secretion proteins. Transport and secretion proteins are essential for moving molecules across cell membranes, within cells, and for exporting substances outside the cell. These proteins are involved in nutrient uptake, waste removal, intracellular trafficking, and secretion of molecules, all crucial for cellular function and communication. NolW-like protein from BEH39788.1 involved in type II and III secretion systems, facilitating protein transport [ 57 ]. Periplasmic binding protein-like I domain was expressed by BEH38318.1 that binds small molecules in the periplasm, facilitating transport into the cell [ 58 ]. Polysaccharide biosynthesis and transport domain from BEH38786.1 responsible for synthesizing and transporting polysaccharides [ 59 – 60 ] BEH38270.1, expressed enzyme (Lipopolysaccharide/Lipooligosaccharide Heptosyltransferase) that adds heptose to lipopolysaccharides [ 61 ]. BEH38435.1 has Trimeric autotransporter adhesin YadA-like head domain is responsible for the autotransport function [ 62 ]. e. Binding and Regulatory Protein There were three proteins of this type. Binding and regulatory proteins are essential for controlling and stabilizing molecular interactions within the cell and orchestrating cellular processes by regulating the activity, localization, and concentration of other molecules. They play key roles in processes such as signal transduction, gene expression, and metabolic regulation. Galactose-binding-like domain superfamily was expressed by BEH40516.1 this domain recognizes and bind carbohydrates. BEH38518.1 classified as Transcription regulator HTH, LysR DNA-binding proteins that regulate gene expression [ 63 ]. BEH40504.1 has Tetratricopeptide-like helical domain, function in protein binding [ 64 ]. f. Electron Transport and Oxidative Enzymes BEH39854.1 was classified as Cytochrome c oxidase-like domain that expressed as enzymes, are components of the electron transport chain, involved in cellular respiration [ 65 – 68 ]. g. PIN Domain Proteins The PIN domain (PilT N-terminus) is a conserved protein domain found in various organisms, including bacteria, archaea, and eukaryotes [ 69 ]. Proteins containing the PIN domain play essential roles in RNA metabolism and defense mechanisms within cells [ 70 ]. The domain is named after its presence in the PilT protein, which is involved in bacterial pilus formation [ 71 ], but it is widely recognized for its function in ribonuclease activity [ 72 ]. PIN-like domain was expressed by BEH38253.1 protein often associated with RNase activity, involved in RNA processing [ 73 ]. 3.2 Prediction of Physiocochemical Properties The physicochemical properties of hypothetical proteins were tabulated in Table S2. The majority of the proteins analyzed had molecular weight (MW) values exceeding 10,000 Da. Proteins with a lower MW (< 10,000 Da) require specific modifications for analysis in the SDS-PAGE system [ 74 ]. Therefore, the initial few HPs with lower MW values will need special consideration for subsequent laboratory experiments. The pH value of a protein at which it carries no net electrical charge is known as isoelectric point pI. For our selected HPs, it ranged from 4.9 to 11and 13 proteins have acidic nature (pI < 7), whereas others were found to be basic. Proteins achieve stability and compactness at their isoelectric pH; thus, the calculated pI will aid in designing an effective buffer system for protein purification [ 75 ]. Although ExPasy’s ProtParam calculates the extinction coefficient at various wavelengths (276, 278, 279, 280, and 282 nm), 280 nm is preferred as proteins strongly absorb light at this wavelength, whereas other common substances in protein solutions do not. The extinction coefficient of hypothetical protein homologues at 280 nm ranges from 13200 to 164210 M cm, depending on the concentration of Cys, Trp, and Tyr. A high extinction coefficient in these proteins suggests a substantial presence of Cys, Trp, and Tyr residues. The calculated extinction coefficients are useful for quantitative analysis of protein-protein and protein-ligand interactions in solution. The instability index provides an estimate of a protein's stability in vitro. Certain dipeptides are found to occur significantly more frequently in unstable proteins than in stable ones, allowing for the assignment of weight values related to instability. These weight values enable the calculation of an instability index (II). A protein with an instability index below 40 is predicted to be stable, while a value above 40 suggests potential instability. The instability index for the hypothetical proteins ranged from 8.25 to 67.06. 13 proteins were identified as stable, whereas the remaining proteins were classified as unstable. The aliphatic index (AI) is defined as the relative volume of a protein occupied by aliphatic side chains (A, V, I, and L) and is considered a positive factor in enhancing the thermal stability of globular proteins. For the hypothetical proteins (HPs), the AI values range from 61.38 to 123.22. Proteins with a very high AI are likely to exhibit stability across a broad temperature range, whereas those with lower AI values are generally less thermally stable and display greater structural flexibility compared to other proteins [ 76 ]. The Grand Average of Hydropathicity (GRAVY) values for the hypothetical proteins (HPs) range from − 0.741 to 0.533, calculated by summing the hydropathy values of all amino acids and dividing by the number of residues in each sequence. GRAVY scores below 0 suggest that a protein is likely hydrophilic (globular), while scores above 0 indicate a hydrophobic (membrane-associated) protein [ 77 ]. A total of 16 HPs were classified as hydrophilic, while 11 HPs were identified as hydrophobic. 3.3 Prediction of Subcellular Localization Determining the subcellular localization of proteins is essential for deciphering their functional roles in cellular processes [ 78 ]. Comprehensive knowledge of protein localization within cellular compartments can significantly enhance the accuracy of target identification, thereby advancing the drug discovery process. CELLO predicted the localization site of the hypothetical proteins selected in this study and they were tabulated in Table S3. Among the 27 HPs, most of the proteins were determined as cytoplasmic. Several cytoplasmic proteins are in the regulation of several functional processes including biosynthesis, regulatory activities, and transport which may help environmental bacteria to compete with the neighboring organisms in the same ecological niche [ 79 ]. 3.4 Prediction of Transmembrane Protein Additionally, we only found 5 membrane-bound proteins. Membrane-spanning proteins are reported to be involved in signaling pathways and the transportation of nutrients across different biological environments within and outside the cell [ 80 ]. For this reason, it is important to identify the presence of signal peptides and transmembrane helices within the HPs. Table S4 highlights the prediction of transmembrane helices and their position using TMHMM. 3.5 Homology Based Tertiary Structure (3D) Prediction and Validation The SWISS-MODEL server searched for structural templates and built the model on the basis of homology. The typical rule for modelling a protein is that there must be minimum 30% identity between the template and the target. Following this study, we employed the protein structure homology-modeling server SWISS-MODEL searched for templates for of 27 HPs. Only the templates determined through high resolution of X-ray crystallography structure were employed. We successfully constructed 3D models for six HPs (BEH39920.1, BEH35991.1, BEH39008.1, BEH35088.1, BEH40390.1, BEH39854.1) with sequence identity above 30% and Ramachandran favored percentages exceeding 90%, as validated by PROCHECK. All of the models were visualized and superimposed in PyMOL software. For instance, we obtained the structure of BEH39920.1 using the X-ray structure of TssM A USP like DUB from B. pseudomallei at 3.15 Aº resolutions as template (Fig. 3 ). The sequence identity was found 80.73%, which is highest among 6 models. The qualility of the models were assessed using PROCHRCK Ramachandran plots, ERRAT and VERIFY 3D of SAVES v.6.0 server. In Ramachandran plots, 92.5% of the amino acid residues were found in favoured regions, which is considered a good quality (Fig. 4 a). ERRAT confirmed the overall quality factor for the modeled structures, it revealed that quality was 92.1348%. Moreover, VARIFY 3D analysis showed that 92.50% of the residues have averaged 3D-1D score ≥ 0.1. Finally, QMEANDisCo Global score and QMEAN Z-score (Fig. 4 b) was found 0.83 and 0 to 1 (Table S5). Overall, all of the modeled structures has Ramachandran favoured region > 90%, ERRAT quality factor was > 90%. The models of BEH40390.1, BEH39854.1, BEH35991.1, BEH39008.1 were failed in VERIFY 3D with fewer than 80% of the amino acids having a scores of ≥ 0.1. The rest of the two models: BEH35088.1 and BEH39920.1 (Figures were passed in VARIFY 3D more than 85% of residues had an averaged 3D-1D scores of ≥ 0.1. Then, a QMEANDisCo Global score of all models were 0.73 up to 0.83, which indicates a good quality. the QMEAN Z-score shows how well the model matches an experimental structure of a similar size. When the QMEAN Z-score is close to zero, it signifies good agreements, models with a score below − 4.0 are considered low quality. The model of BEH39920.1, BEH35088.1 has Z-scores within the range of 0 to 1 (0–1.0); the model of BEH38435.1,BEH39008.1 has Z-scores between 1 and 2 (1.0–2.0); BEH40390.1, BEH39854.1 scored > 2. 3.6 Prediction of Ligand Binding Sites The prediction of ligand binding site determination on protein is important for a wide range of applications including structural identification, comparison of functional sites, molecular docking and de novo drug design. Here, we determined the location of ligand binding sited of 6 modeled hypothetical protein. The ligand binding site residues of the HPs are mentioned in Table S6. This information on binding site residues will help detect binding interactions and docking with specific ligand. Hypothetical protein BEH39920.1 has ubiquitin C-terminal hydrolase activity, which promotes the invasion of epithelial cells by Listeria monocytogenes and Salmonella enterica [ 46 ] also, the proteins also has role in oncogenesis [ 81 ]. CASE STUDIES 3.7 Protein protein Interaction Several interacting protein of our target protein were estimated using STRING database. We used Burkholderia pseudomallei strain K96243 as a reference organism to identify protein protein interactions (PPI). The interacting proteins included BPSS1513, folE-2, BPSL2150, BPSS0180, BPSS1521x BPSS2096, BPSL0707, BPSS1520, BPSL1929 with interaction score of above 4 (Fig. 5 ). Their molecular functions are mentioned in Table S7. 3.8 Multiple Sequence Alignment and Phylogenctic Analysis BEH39920.1, we blast the sequence of this hypothetical protein in BLASTp search of NCBI against the non-rebundant protein database for finding the homologs of the protein. We collected 10 hits with sequence identity ≥ 99%. Target sequece of protein and 10 sequences of homologs are aligned using Clustal Omega. Then phologenetic tree was made which probides evolutionary relationship of the target protein and with closed related species (Fig. 6 ). 3.9 Motif analysis A total of four motifs were highly conserved across all the 10 homologous sequences. The HP lacked motif 5 to 10, which was mostly conserved among other strain of this organism shown in Fig. 7 . 4. Conclusion This study employed comprehensive in silico approaches to functionally annotate 27 hypothetical proteins (HPs) from Burkholderia pseudomallei strain GTC3P0254T, shedding light on their potential roles in bacterial metabolism, virulence, and antimicrobial resistance. Functional annotation revealed diverse biological activities, including enzymatic functions (e.g., polyphosphate kinase, isoprenoid synthase, and ubiquitin hydrolase), structural/adhesion roles, DNA/RNA processing, transport, and regulatory mechanisms. Notably, BEH39920.1, predicted to possess ubiquitin hydrolase activity, may contribute to host cell invasion—a critical virulence factor—while membrane-associated proteins like BEH38270.1 (heptosyltransferase) and BEH37677.1 (TagF-like enzyme) were linked to lipopolysaccharide biosynthesis and teichoic acid polymerization, processes central to drug resistance and host adaptation. Structural modeling and validation highlighted the reliability of homology-based predictions for six HPs, with BEH39920.1 and BEH35088.1 exhibiting high-quality 3D models (Ramachandran favored residues > 90%, ERRAT > 92%). Protein-protein interaction (PPI) networks further contextualized these HPs within pathways involving stress response, DNA repair, and secretion systems, underscoring their functional interconnectivity. Phylogenetic and motif analyses emphasized evolutionary conservation and domain-specific roles, particularly for proteins like BEH39920.1, which clustered closely with homologs from pathogenic Burkholderia species. While these computational insights provide a robust framework for understanding HP functions, experimental validation through biochemical assays (e.g., enzyme activity tests), genetic knockouts, and infection models is imperative to confirm their roles in pathogenicity. Additionally, the exclusion of 59 HPs due to ambiguous annotations highlights the need for advanced tools to resolve poorly conserved domains. The identification of HPs involved in critical pathways—such as polyphosphate metabolism, virulence, and antibiotic resistance—positions them as promising candidates for therapeutic targeting. Future studies should prioritize these proteins for drug discovery and diagnostic biomarker development, potentially mitigating the high mortality and treatment challenges associated with melioidosis. Integrating computational predictions with experimental validation will accelerate the translation of these findings into clinical applications, offering new strategies to combat this resilient pathogen. Declarations Acknowledgments We sincerely thank Assistant Professor S.C. Das, Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, for his valuable guidance, constructive feedback, and continuous support throughout this study. Authors’ contributions MH, MS, SA, and JF contributed to the study's conception, data analysis, manuscript writing, and revision. All authors read and approved the final manuscript. Funding This research received no specific grant from any funding agency, commercial, or not-for-profit sectors. Competing Interests The authors declare that they have no competing interests. References Limmathurotsakul D, Golding N, Dance DA, et al. Predicted global distribution of Burkholderia pseudomallei and burden of melioidosis. Nat Microbiol . 2016;1(1):15008. doi:10.1038/nmicrobiol.2015.8 Wuthiekanun V, Peacock SJ. Management of melioidosis. Expert Rev Anti Infect Ther . 2006;4(3):445-455. doi:10.1586/14787210.4.3.445 Chen YS, Chen SC, Kao CM, Chen YL. 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Biochim Biophys Acta . 2010;1806(1):1-6. doi:10.1016/j.bbcan.2010.03.001 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterials.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6292329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432965997,"identity":"f8a05fde-1a05-4fbc-b924-d0927ed5dbaf","order_by":0,"name":"Mahbub Hossain","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-0985-9639","institution":"Noakhali Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Mahbub","middleName":"","lastName":"Hossain","suffix":""},{"id":432965998,"identity":"6ca4b86d-90bc-4852-8663-69ca311951f9","order_by":1,"name":"Md. Saiful","email":"","orcid":"","institution":"Noakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Saiful","suffix":""},{"id":432965999,"identity":"2368a51b-dd7d-4e48-9c95-1129e09a10a5","order_by":2,"name":"Sumaya Afroz","email":"","orcid":"","institution":"Noakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Sumaya","middleName":"","lastName":"Afroz","suffix":""},{"id":432966000,"identity":"903e4b3b-0ea5-45d7-89a9-adc4722f7b29","order_by":3,"name":"Jannatul Ferdaous","email":"","orcid":"","institution":"Noakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Jannatul","middleName":"","lastName":"Ferdaous","suffix":""}],"badges":[],"createdAt":"2025-03-24 06:52:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6292329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6292329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79154440,"identity":"7fac266d-00ae-4dd8-b0fc-7e261b61d66a","added_by":"auto","created_at":"2025-03-25 05:42:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":524564,"visible":true,"origin":"","legend":"\u003cp\u003eThe research workflow for analyzing hypothetical proteins (HPs) from \u003cem\u003eBurkholderia\u003c/em\u003e \u003cem\u003epseudomallei\u003c/em\u003e. Phase I involved retrieving 86 HPs and selecting 27 with significant functional predictions using InterPro, SMART, and CDD. Phase II included detailed analyses of physicochemical properties, subcellular localization, transmembrane helices, tertiary structure, and ligand-binding sites. The Case Study phase focused on protein-protein interactions (STRING), multiple sequence alignment, phylogenetic analysis, and motif identification (MEME)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/cb10da91a7adc811269ec18b.png"},{"id":79153814,"identity":"b1faf990-1654-4bc6-b54b-69cd979732c8","added_by":"auto","created_at":"2025-03-25 05:34:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":637161,"visible":true,"origin":"","legend":"\u003cp\u003eA total of 27 out of 86 HPs classified into respective functional groups based on functional annotations using various bioinformatic tools.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/28b39db5e3d93e0557ff3417.jpeg"},{"id":79153823,"identity":"ab4174ec-5691-412a-9d7f-db044ef039e9","added_by":"auto","created_at":"2025-03-25 05:34:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3876387,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of tertiary structure of 6 hypothetical proteins. 3D structures were modeled using SWISS-MODEL server. X-ray crystallography structure of protein with high resolution were used as templates. The templates with higher coverage and sequence identity over 30% were used. Only the models with ramachandran favored percentages above 90% were chosen, also further assessed using ERRAT, VARIFY3D, QMEAN value and Z-scores of SWISS-MODEL server. The models were visualized in PyMOL software and superimposed with their templates. The templates and model structures are shown in red and blue respectively. The combination of red and blue indicates their corresponding super imposed structures.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/f0253480035e3ad4915628b0.png"},{"id":79154441,"identity":"f8ecd34f-c917-4e0d-a171-3c63c8ce8c9b","added_by":"auto","created_at":"2025-03-25 05:42:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":476974,"visible":true,"origin":"","legend":"\u003cp\u003ea \u0026amp; b. Quality assessment of the modeled protein (BEH39920.1). Ramachandran plot of the modeled structure valided by PROCHECK and QMEAN Z-Score result of SWISS-MODEL servers, indicated good agreement between the modeled Structure and the experimental structure.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/16b54dd0abe2acda01a9a45c.jpeg"},{"id":79153816,"identity":"7defe871-6b5a-4f54-9096-9206dd291170","added_by":"auto","created_at":"2025-03-25 05:34:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":107155,"visible":true,"origin":"","legend":"\u003cp\u003eProtein protein interaction of BEH39929.1\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/ce268464eefccec96b7aa773.jpeg"},{"id":79155445,"identity":"5d3c0dd6-0b8e-4176-afef-81b03ec428b5","added_by":"auto","created_at":"2025-03-25 06:06:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25273,"visible":true,"origin":"","legend":"\u003cp\u003eThen phylogenetic tree was made which provides evolutionary relationship of the target protein and with closed related species.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/8bcf623af02be551de2abd90.png"},{"id":79153834,"identity":"d9a4d7e7-947c-4e30-a89c-7023f10f19d7","added_by":"auto","created_at":"2025-03-25 05:34:54","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":691944,"visible":true,"origin":"","legend":"\u003cp\u003eThe motif analysis assessment of BEH39920.1 predicted it as a Ig-like domain-containing protein located at the top. The motif analysis heatmap generated from MEME Suite highlighting 4 highly conserved motifs across the 10 homologs listed in the legend shows the similarity among these samples.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/84cb4091508d07e483a1ed99.jpeg"},{"id":79155488,"identity":"b3688044-0afe-445a-9567-92e692c5bc16","added_by":"auto","created_at":"2025-03-25 06:07:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6878996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/a268bd64-27c9-4f11-b870-1037fe4522fd.pdf"},{"id":79155444,"identity":"d2ae74d9-06ba-4714-8caa-bfe1acab8770","added_by":"auto","created_at":"2025-03-25 06:06:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20593,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6292329/v1/89e789f1802aad6f3eef5551.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIn Silico Functional Annotation of Hypothetical Proteins from Burkholderia pseudomallei strain GTC3P0254T\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e is a Gram-negative, aerobic, soil-dwelling bacterium responsible for melioidosis, a disease predominantly found in Southeast Asia and Northern Australia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease has a mortality rate of 20\u0026ndash;50% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cem\u003eB. pseudomallei\u003c/em\u003e thrives at an optimal temperature of 37\u0026deg;C and a pH range of 6.5\u0026ndash;7.5 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to its high mortality rate, heat resistance, acid tolerance, and airborne transmission capability, the Centers for Disease Control and Prevention (CDC) has classified it as a Tier 1 Select Agent [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The bacterium can also infect animals, particularly livestock such as sheep, pigs, and goats [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is inherently resistant to gentamicin [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and colistin, aiding its identification in clinical settings [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although kanamycin is used in laboratory settings to eliminate \u003cem\u003eB. pseudomallei\u003c/em\u003e, the concentrations required far exceed what is feasible in humans [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several vaccine candidates have been assessed in preclinical research, but as of 2023, none have been licensed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genome of \u003cem\u003eB. pseudomallei\u003c/em\u003e strain GTC3P0254T has been fully sequenced, consisting of two chromosomes of 3.93 Mb and 3.14 Mb, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to the National Center for Biotechnology Information (NCBI) database, the genome encodes 6,077 protein-coding genes. However, approximately 16% of these are predicted to encode proteins for which no experimental expression or functional data are available\u0026mdash;referred to as hypothetical proteins (HPs). Given their unknown roles, functional annotation of these proteins is essential to determine their physicochemical properties, subcellular localization, tertiary structures, ligand-binding sites, sequence alignments, phylogenetic relationships, and motif structures using various bioinformatics tools.\u003c/p\u003e \u003cp\u003eThis study evaluated 86 HPs out of 974 identified in \u003cem\u003eB. pseudomallei\u003c/em\u003e. Through functional annotation using InterPro [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], SMART [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the Conserved Domain Database (CDD) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], we successfully predicted the functions of 27 HPs. These were classified as binding proteins (including DNA- and RNA-binding proteins, cofactors, and membrane-bound proteins), enzymes (involved in metabolism, biosynthesis, and survival), regulatory proteins, and one structural protein. A range of bioinformatics tools was utilized, including Expasy ProtParam [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for physicochemical property prediction, PSORTb [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], CELLO [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and TMHMM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for subcellular localization, and MEME [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] for motif analysis. Additionally, Clustal Omega [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was used for multiple sequence alignment and phylogenetic analysis, while SWISS-MODEL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was employed for homology modeling and structure validation.\u003c/p\u003e \u003cp\u003eBy elucidating the possible functions of these HPs, this study provides a foundation for future research into their potential applications in clinical and biotechnological fields.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sequence Retrieval\u003c/h2\u003e \u003cp\u003eThe genome of \u003cem\u003eB. pseudomallei\u003c/em\u003e strain GTC3P0254T comprises two chromosomes with a total length of 7.1 Mb [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], encoding 6,077 protein-coding genes. Among these, 974 were identified as hypothetical proteins. A subset of 86 HPs was selected for functional analysis, out of which 27 were successfully annotated based on domain identification using InterPro, SMART, and CDD. Proteins lacking conserved domains or motifs with sufficient evidence for biological role prediction (59 HPs) were excluded due to ambiguous annotations. The selected sequences were retrieved in FASTA format from the NCBI database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Functional Annotation of Hypothetical Proteins\u003c/h2\u003e \u003cp\u003eFunctional domains of selected HPs were predicted using InterPro [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], SMART [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the Conserved Domain Database (CDD) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These tools enabled the identification of conserved protein motifs and domains, facilitating classification into functional groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Prediction of Physicochemical Properties\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the 27 HPs were computed using Expasy\u0026rsquo;s ProtParam tool. Key parameters, including molecular weight, theoretical isoelectric point (pI), charge distribution, extinction coefficient [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], instability index [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], aliphatic index (AI), and grand average of hydropathicity (GRAVY) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], were calculated to assess protein stability and solubility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Prediction of the Subcellular Localization\u003c/h2\u003e \u003cp\u003eThe subcellular localization of HPs was predicted using PSORTb [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], CELLO [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and TMHMM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PSORTb is an open-source tool for bacterial and archaeal localization prediction, while CELLO uses a support vector machine-based approach for cellular compartment prediction. TMHMM was used to identify transmembrane helices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Prediction of Transmembrane Proteins\u003c/h2\u003e \u003cp\u003eTMHMM 2.0 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used to predict transmembrane helices and distinguish membrane-bound from soluble proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Predicting Tertiary Structure of the Protein\u003c/h2\u003e \u003cp\u003eTertiary structures were modeled using SWISS-MODEL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a homology-based server. Templates with \u0026gt;\u0026thinsp;30% sequence identity and high-resolution X-ray structures were prioritized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Quality Assessment\u003c/h2\u003e \u003cp\u003eStructural validation was performed using PROCHECK [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], ERRAT [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], VERIFY3D [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], QMEANDisCo [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and QMEAN Z-scores [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Models with \u0026gt;\u0026thinsp;90% Ramachandran-favored residues and ERRAT scores\u0026thinsp;\u0026gt;\u0026thinsp;90% were retained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Prediction of Ligand Binding Sites\u003c/h2\u003e \u003cp\u003eThe prediction of ligand binding sites were determined using COACH server [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The COACH consensus algorithm is among the most effective methods for predicting protein-ligand interaction sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Prediction of Protein Protein Interaction\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) networks were analyzed using the STRING database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which integrates functional and physical interactions based on genomic context, co-expression, curated databases, and text mining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Multiple Sequence Alignment and phylogenetic analysis\u003c/h2\u003e \u003cp\u003eMultiple sequence alignment and phylogenetic analyses were performed using Clustal Omega [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to determine evolutionary relationships between HPs and homologous proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Motif Analysis.\u003c/h2\u003e \u003cp\u003eMotif analysis was conducted using the MEME Suite v5.5.7 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to identify conserved sequence motifs across homologous proteins.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cp\u003e \u003cb\u003ePhase I and II\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Functional Annotation of Hypothetical Proteins\u003c/h2\u003e \u003cp\u003eDomains are distinct functional and/or structural units in a protein. Usually, they are responsible for a particular function or interaction, contributing to the overall role of a protein. Here in the following table respective functions of 27 HPs are determined. Then classified on the basis of functional annotation using various online tools ( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), functions are mentioned in Table S1. (Supplementary materials)\u003c/p\u003e \u003cp\u003e \u003cb\u003ea. Enzymatic Proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEnzymes are biological molecules, usually proteins, that act as catalysts in biochemical reactions, meaning they speed up chemical reactions without being consumed in the process. Enzymes are essential for many biological functions, such as digestion, energy production, DNA replication, and more. Among the 27 hypothetical proteins, eight were predicted to have enzymatic functions. BEH40390.1 classified as Tagatose/fructose phosphokinase, it would be catalyzing the phosphorylation of tagatose and fructose [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. BEH40255.1, this hypothetical protein had O-antigen ligase domain, involved in the assembly of O-antigens in lipopolysaccharides [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and BEH35088.1 (Polyphosphate kinase activity) protein\u0026rsquo;s domain play role for synthesizing polyphosphate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] BEH39008.1 protein was predicted to be have isoprenoid synthase domain that synthesizes isoprenoids, crucial for various biosynthetic pathways [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. P-loop containing nucleoside triphosphate hydrolase domain expressed by BEH37235.1 hypothetical protein that hydrolyzes nucleoside triphosphates, involved in energy metabolism [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. One HP BEH37114.1 was classified as NAD(P)-binding domain superfamily that utilize NAD(P) as a cofactor in various metabolic reactions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. BEH37677.1 (CDP-glycerol:poly(glycerophosphate) glycerophosphotransferase TagF) is essential for wall teichoic acid polymerization, catalyzing the sequential transfer of glycerol-phosphate units from CDP-glycerol to the linkage unit lipid [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It has been demonstrated that the TagF-like family of enzymes is in charge of wall teichoic acid priming and polymerization processes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. BEH35088.1, this protein has polyphosphate kinase activity. In M. tuberculosis, it is important for its survival in macrophages [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. BEH39920.1 has ubiquitin carboxyl-terminal hydrolase activity and SbsA, Ig-like domain. The ubiquitin C-terminal hydrolase activity, which promotes the invasion of epithelial cells by Listeria monocytogenes and Salmonella enterica [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Here, we case studied of this protein considering its virulence and target for therapeutic drugs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eb. Structural and Adhesion Proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStructural and adhesion proteins are vital components in cells and tissues, playing crucial roles in providing structural integrity and mediating interactions between cells or between cells and the extracellular matrix (ECM). There were four structural and adhesion proteins. BEH40411.1 expressed Bacterial collagen-like proteins domain contributing to bacterial adhesion and cell integrity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. BEH39177.1 (Trimeric autotransporter adhesin YadA, Serralysin-like metalloprotease domain) protein that combines structural and enzymatic functions for adhesion and proteolytic activity [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. BEH39837.1 was classified as YadA-like membrane anchor domain that was membrane-anchoring domain facilitating bacterial adhesion. Six-bladed beta-propeller, TolB-like structural domain expressed by BEH40106.1 often involved in protein-protein interactions [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ec. DNA and RNA Processing Proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDNA and RNA processing proteins are essential for managing the integrity, replication, and expression of genetic information within cells. These proteins are involved in several critical processes, such as DNA replication and repair, RNA transcription, splicing, editing, and stability. Five proteins of this type were identified. FtsK/SpoIIIE/SftA domain (BEH35991.1) involved in bacterial cell division and DNA segregation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. BEH39282.1 classified as Endonuclease GajA/Old nuclease/RecF-like, AAA domain, ATPase, AAA-type, core which was involved in DNA repair and processing [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Restriction endonuclease type II-like, tRNA endonuclease-like domain superfamily was expressed by BEH39277.1 that cleave DNA and RNA, critical for restriction-modification systems [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. BEH38760.1 (UvrD-like helicase C-terminal domain) involved in DNA repair. Relaxes domain of MobM and similar proteins expressed by BEH38753.1 which Facilitates DNA relaxation during plasmid transfer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cb\u003ed. Transport and Secretion Proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFive proteins out of 27 were transport and secretion proteins. Transport and secretion proteins are essential for moving molecules across cell membranes, within cells, and for exporting substances outside the cell. These proteins are involved in nutrient uptake, waste removal, intracellular trafficking, and secretion of molecules, all crucial for cellular function and communication. NolW-like protein from BEH39788.1 involved in type II and III secretion systems, facilitating protein transport [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Periplasmic binding protein-like I domain was expressed by BEH38318.1 that binds small molecules in the periplasm, facilitating transport into the cell [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Polysaccharide biosynthesis and transport domain from BEH38786.1 responsible for synthesizing and transporting polysaccharides [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] BEH38270.1, expressed enzyme (Lipopolysaccharide/Lipooligosaccharide Heptosyltransferase) that adds heptose to lipopolysaccharides [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. BEH38435.1 has Trimeric autotransporter adhesin YadA-like head domain is responsible for the autotransport function [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003ee. Binding and Regulatory Protein\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were three proteins of this type. Binding and regulatory proteins are essential for controlling and stabilizing molecular interactions within the cell and orchestrating cellular processes by regulating the activity, localization, and concentration of other molecules. They play key roles in processes such as signal transduction, gene expression, and metabolic regulation. Galactose-binding-like domain superfamily was expressed by BEH40516.1 this domain recognizes and bind carbohydrates. BEH38518.1 classified as Transcription regulator HTH, LysR DNA-binding proteins that regulate gene expression [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. BEH40504.1 has Tetratricopeptide-like helical domain, function in protein binding [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003ef. Electron Transport and Oxidative Enzymes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBEH39854.1 was classified as Cytochrome c oxidase-like domain that expressed as enzymes, are components of the electron transport chain, involved in cellular respiration [\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eg. PIN Domain Proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe PIN domain (PilT N-terminus) is a conserved protein domain found in various organisms, including bacteria, archaea, and eukaryotes [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Proteins containing the PIN domain play essential roles in RNA metabolism and defense mechanisms within cells [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The domain is named after its presence in the PilT protein, which is involved in bacterial pilus formation [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], but it is widely recognized for its function in ribonuclease activity [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. PIN-like domain was expressed by BEH38253.1 protein often associated with RNase activity, involved in RNA processing [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prediction of Physiocochemical Properties\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of hypothetical proteins were tabulated in Table S2. The majority of the proteins analyzed had molecular weight (MW) values exceeding 10,000 Da. Proteins with a lower MW (\u0026lt;\u0026thinsp;10,000 Da) require specific modifications for analysis in the SDS-PAGE system [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Therefore, the initial few HPs with lower MW values will need special consideration for subsequent laboratory experiments. The pH value of a protein at which it carries no net electrical charge is known as isoelectric point pI. For our selected HPs, it ranged from 4.9 to 11and 13 proteins have acidic nature (pI\u0026thinsp;\u0026lt;\u0026thinsp;7), whereas others were found to be basic. Proteins achieve stability and compactness at their isoelectric pH; thus, the calculated pI will aid in designing an effective buffer system for protein purification [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough ExPasy\u0026rsquo;s ProtParam calculates the extinction coefficient at various wavelengths (276, 278, 279, 280, and 282 nm), 280 nm is preferred as proteins strongly absorb light at this wavelength, whereas other common substances in protein solutions do not. The extinction coefficient of hypothetical protein homologues at 280 nm ranges from 13200 to 164210 M cm, depending on the concentration of Cys, Trp, and Tyr. A high extinction coefficient in these proteins suggests a substantial presence of Cys, Trp, and Tyr residues. The calculated extinction coefficients are useful for quantitative analysis of protein-protein and protein-ligand interactions in solution.\u003c/p\u003e \u003cp\u003eThe instability index provides an estimate of a protein's stability in vitro. Certain dipeptides are found to occur significantly more frequently in unstable proteins than in stable ones, allowing for the assignment of weight values related to instability. These weight values enable the calculation of an instability index (II). A protein with an instability index below 40 is predicted to be stable, while a value above 40 suggests potential instability. The instability index for the hypothetical proteins ranged from 8.25 to 67.06. 13 proteins were identified as stable, whereas the remaining proteins were classified as unstable.\u003c/p\u003e \u003cp\u003eThe aliphatic index (AI) is defined as the relative volume of a protein occupied by aliphatic side chains (A, V, I, and L) and is considered a positive factor in enhancing the thermal stability of globular proteins. For the hypothetical proteins (HPs), the AI values range from 61.38 to 123.22. Proteins with a very high AI are likely to exhibit stability across a broad temperature range, whereas those with lower AI values are generally less thermally stable and display greater structural flexibility compared to other proteins [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Grand Average of Hydropathicity (GRAVY) values for the hypothetical proteins (HPs) range from \u0026minus;\u0026thinsp;0.741 to 0.533, calculated by summing the hydropathy values of all amino acids and dividing by the number of residues in each sequence. GRAVY scores below 0 suggest that a protein is likely hydrophilic (globular), while scores above 0 indicate a hydrophobic (membrane-associated) protein [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. A total of 16 HPs were classified as hydrophilic, while 11 HPs were identified as hydrophobic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Prediction of Subcellular Localization\u003c/h2\u003e \u003cp\u003eDetermining the subcellular localization of proteins is essential for deciphering their functional roles in cellular processes [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Comprehensive knowledge of protein localization within cellular compartments can significantly enhance the accuracy of target identification, thereby advancing the drug discovery process. CELLO predicted the localization site of the hypothetical proteins selected in this study and they were tabulated in Table S3. Among the 27 HPs, most of the proteins were determined as cytoplasmic. Several cytoplasmic proteins are in the regulation of several functional processes including biosynthesis, regulatory activities, and transport which may help environmental bacteria to compete with the neighboring organisms in the same ecological niche [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Prediction of Transmembrane Protein\u003c/h2\u003e \u003cp\u003eAdditionally, we only found 5 membrane-bound proteins. Membrane-spanning proteins are reported to be involved in signaling pathways and the transportation of nutrients across different biological environments within and outside the cell [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. For this reason, it is important to identify the presence of signal peptides and transmembrane helices within the HPs. Table S4 highlights the prediction of transmembrane helices and their position using TMHMM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Homology Based Tertiary Structure (3D) Prediction and Validation\u003c/h2\u003e \u003cp\u003eThe SWISS-MODEL server searched for structural templates and built the model on the basis of homology. The typical rule for modelling a protein is that there must be minimum 30% identity between the template and the target. Following this study, we employed the protein structure homology-modeling server SWISS-MODEL searched for templates for of 27 HPs. Only the templates determined through high resolution of X-ray crystallography structure were employed. We successfully constructed 3D models for six HPs (BEH39920.1, BEH35991.1, BEH39008.1, BEH35088.1, BEH40390.1, BEH39854.1) with sequence identity above 30% and Ramachandran favored percentages exceeding 90%, as validated by PROCHECK. All of the models were visualized and superimposed in PyMOL software.\u003c/p\u003e \u003cp\u003eFor instance, we obtained the structure of BEH39920.1 using the X-ray structure of TssM A USP like DUB from \u003cem\u003eB. pseudomallei\u003c/em\u003e at 3.15 A\u0026ordm; resolutions as template (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The sequence identity was found 80.73%, which is highest among 6 models. The qualility of the models were assessed using PROCHRCK Ramachandran plots, ERRAT and VERIFY 3D of SAVES v.6.0 server. In Ramachandran plots, 92.5% of the amino acid residues were found in favoured regions, which is considered a good quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). ERRAT confirmed the overall quality factor for the modeled structures, it revealed that quality was 92.1348%. Moreover, VARIFY 3D analysis showed that 92.50% of the residues have averaged 3D-1D score\u0026thinsp;\u0026ge;\u0026thinsp;0.1. Finally, QMEANDisCo Global score and QMEAN Z-score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) was found 0.83 and 0 to 1 (Table S5).\u003c/p\u003e \u003cp\u003eOverall, all of the modeled structures has Ramachandran favoured region\u0026thinsp;\u0026gt;\u0026thinsp;90%, ERRAT quality factor was \u0026gt;\u0026thinsp;90%. The models of BEH40390.1, BEH39854.1, BEH35991.1, BEH39008.1 were failed in VERIFY 3D with fewer than 80% of the amino acids having a scores of \u0026ge;\u0026thinsp;0.1. The rest of the two models: BEH35088.1 and BEH39920.1 (Figures were passed in VARIFY 3D more than 85% of residues had an averaged 3D-1D scores of \u0026ge;\u0026thinsp;0.1. Then, a QMEANDisCo Global score of all models were 0.73 up to 0.83, which indicates a good quality. the QMEAN Z-score shows how well the model matches an experimental structure of a similar size. When the QMEAN Z-score is close to zero, it signifies good agreements, models with a score below \u0026minus;\u0026thinsp;4.0 are considered low quality. The model of BEH39920.1, BEH35088.1 has Z-scores within the range of 0 to 1 (0\u0026ndash;1.0); the model of BEH38435.1,BEH39008.1 has Z-scores between 1 and 2 (1.0\u0026ndash;2.0); BEH40390.1, BEH39854.1 scored\u0026thinsp;\u0026gt;\u0026thinsp;2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Prediction of Ligand Binding Sites\u003c/h2\u003e \u003cp\u003eThe prediction of ligand binding site determination on protein is important for a wide range of applications including structural identification, comparison of functional sites, molecular docking and de novo drug design. Here, we determined the location of ligand binding sited of 6 modeled hypothetical protein. The ligand binding site residues of the HPs are mentioned in Table S6. This information on binding site residues will help detect binding interactions and docking with specific ligand. Hypothetical protein BEH39920.1 has ubiquitin C-terminal hydrolase activity, which promotes the invasion of epithelial cells by Listeria monocytogenes and \u003cem\u003eSalmonella enterica\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] also, the proteins also has role in oncogenesis [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCASE STUDIES\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Protein protein Interaction\u003c/h2\u003e \u003cp\u003eSeveral interacting protein of our target protein were estimated using STRING database. We used \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e strain K96243 as a reference organism to identify protein protein interactions (PPI). The interacting proteins included BPSS1513, folE-2, BPSL2150, BPSS0180, BPSS1521x BPSS2096, BPSL0707, BPSS1520, BPSL1929 with interaction score of above 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Their molecular functions are mentioned in Table S7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Multiple Sequence Alignment and Phylogenctic Analysis\u003c/h2\u003e \u003cp\u003eBEH39920.1, we blast the sequence of this hypothetical protein in BLASTp search of NCBI against the non-rebundant protein database for finding the homologs of the protein. We collected 10 hits with sequence identity\u0026thinsp;\u0026ge;\u0026thinsp;99%. Target sequece of protein and 10 sequences of homologs are aligned using Clustal Omega.\u003c/p\u003e \u003cp\u003eThen phologenetic tree was made which probides evolutionary relationship of the target protein and with closed related species (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Motif analysis\u003c/h2\u003e \u003cp\u003eA total of four motifs were highly conserved across all the 10 homologous sequences. The HP lacked motif 5 to 10, which was mostly conserved among other strain of this organism shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study employed comprehensive \u003cem\u003ein silico\u003c/em\u003e approaches to functionally annotate 27 hypothetical proteins (HPs) from \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e strain GTC3P0254T, shedding light on their potential roles in bacterial metabolism, virulence, and antimicrobial resistance. Functional annotation revealed diverse biological activities, including enzymatic functions (e.g., polyphosphate kinase, isoprenoid synthase, and ubiquitin hydrolase), structural/adhesion roles, DNA/RNA processing, transport, and regulatory mechanisms. Notably, BEH39920.1, predicted to possess ubiquitin hydrolase activity, may contribute to host cell invasion\u0026mdash;a critical virulence factor\u0026mdash;while membrane-associated proteins like BEH38270.1 (heptosyltransferase) and BEH37677.1 (TagF-like enzyme) were linked to lipopolysaccharide biosynthesis and teichoic acid polymerization, processes central to drug resistance and host adaptation.\u003c/p\u003e \u003cp\u003eStructural modeling and validation highlighted the reliability of homology-based predictions for six HPs, with BEH39920.1 and BEH35088.1 exhibiting high-quality 3D models (Ramachandran favored residues\u0026thinsp;\u0026gt;\u0026thinsp;90%, ERRAT\u0026thinsp;\u0026gt;\u0026thinsp;92%). Protein-protein interaction (PPI) networks further contextualized these HPs within pathways involving stress response, DNA repair, and secretion systems, underscoring their functional interconnectivity. Phylogenetic and motif analyses emphasized evolutionary conservation and domain-specific roles, particularly for proteins like BEH39920.1, which clustered closely with homologs from pathogenic \u003cem\u003eBurkholderia\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eWhile these computational insights provide a robust framework for understanding HP functions, experimental validation through biochemical assays (e.g., enzyme activity tests), genetic knockouts, and infection models is imperative to confirm their roles in pathogenicity. Additionally, the exclusion of 59 HPs due to ambiguous annotations highlights the need for advanced tools to resolve poorly conserved domains.\u003c/p\u003e \u003cp\u003eThe identification of HPs involved in critical pathways\u0026mdash;such as polyphosphate metabolism, virulence, and antibiotic resistance\u0026mdash;positions them as promising candidates for therapeutic targeting. Future studies should prioritize these proteins for drug discovery and diagnostic biomarker development, potentially mitigating the high mortality and treatment challenges associated with melioidosis. Integrating computational predictions with experimental validation will accelerate the translation of these findings into clinical applications, offering new strategies to combat this resilient pathogen.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Assistant Professor S.C. Das, Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, for his valuable guidance, constructive feedback, and continuous support throughout this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH, MS, SA, and JF contributed to the study\u0026apos;s conception, data analysis, manuscript writing, and revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLimmathurotsakul D, Golding N, Dance DA, et al. Predicted global distribution of \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e and burden of melioidosis. \u003cem\u003eNat Microbiol\u003c/em\u003e. 2016;1(1):15008. doi:10.1038/nmicrobiol.2015.8 \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWuthiekanun V, Peacock SJ. 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The potential role of ubiquitin c-terminal hydrolases in oncogenesis. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e. 2010;1806(1):1-6. doi:10.1016/j.bbcan.2010.03.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Noakhali Science and Technology University","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":"Hypothetical proteins (HPs), Domain, Motif, Tertiary structure, Bioinformatics, Pathogenesis, Genomics and proteomics, Computational biology, Melioidosis","lastPublishedDoi":"10.21203/rs.3.rs-6292329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6292329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e, the causative agent of melioidosis, contains numerous hypothetical proteins (HPs) with unknown functions, limiting our understanding of its biology and pathogenicity. This study employed \u003cem\u003ein silico\u003c/em\u003e approaches to functionally annotate 27 HPs from \u003cem\u003eB. pseudomallei\u003c/em\u003e strain GTC3P0254T using domain analysis, physicochemical characterization, structural modeling, and protein-protein interaction predictions. The identified HPs were classified into enzymes, transporters, binding proteins, regulatory proteins, and structural proteins. Notably, several HPs exhibited enzymatic activity, including polyphosphate kinase and isoprenoid synthase, which play crucial roles in bacterial metabolism and survival. Additionally, membrane-associated proteins were linked to drug resistance and host adaptation, while one HP demonstrated ubiquitin hydrolase activity, a function associated with bacterial invasion and virulence. Homology-based tertiary structure predictions were validated using multiple structural assessment tools, and protein-protein interaction analyses provided insights into their functional associations. These findings enhance our understanding of \u003cem\u003eB. pseudomallei\u003c/em\u003e pathogenesis and antimicrobial resistance, highlighting potential targets for therapeutic interventions. However, since this study is based solely on computational predictions, experimental validation through biochemical assays and genetic studies is essential to confirm these findings. Future research should explore these HPs as potential drug targets and diagnostic biomarkers to improve treatment strategies for melioidosis.\u003c/p\u003e","manuscriptTitle":"In Silico Functional Annotation of Hypothetical Proteins from Burkholderia pseudomallei strain GTC3P0254T","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 05:34:45","doi":"10.21203/rs.3.rs-6292329/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":"372b2223-77ca-4bdb-8aa8-134a4572bfdf","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46160221,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-03-25T05:34:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 05:34:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6292329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6292329","identity":"rs-6292329","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}