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Genomics Inform > Volume 14(3); 2016 > Article
Naqvi, Anjum, Khan, Islam, Ahmad, and Hassan: Sequence Analysis of Hypothetical Proteins from Helicobacter pylori 26695 to Identify Potential Virulence Factors

Abstract

Helicobacter pylori is a Gram-negative bacteria that is responsible for gastritis in human. Its spiral flagellated body helps in locomotion and colonization in the host environment. It is capable of living in the highly acidic environment of the stomach with the help of acid adaptive genes. The genome of H. pylori 26695 strain contains 1,555 coding genes that encode 1,445 proteins. Out of these, 340 proteins are characterized as hypothetical proteins (HP). This study involves extensive analysis of the HPs using an established pipeline which comprises various bioinformatics tools and databases to find out probable functions of the HPs and identification of virulence factors. After extensive analysis of all the 340 HPs, we found that 104 HPs are showing characteristic similarities with the proteins with known functions. Thus, on the basis of such similarities, we assigned probable functions to 104 HPs with high confidence and precision. All the predicted HPs contain representative members of diverse functional classes of proteins such as enzymes, transporters, binding proteins, regulatory proteins, proteins involved in cellular processes and other proteins with miscellaneous functions. Therefore, we classified 104 HPs into aforementioned functional groups. During the virulence factors analysis of the HPs, we found 11 HPs are showing significant virulence. The identification of virulence proteins with the help their predicted functions may pave the way for drug target estimation and development of effective drug to counter the activity of that protein.

Introduction

Helicobacter pylori is a Gram-negative bacteria that is associated with several gastric problems in human. It is a slow growing microaerophilic bacteria [1]. Its spiral shape flagellated body helps in locomotion and invasion on the host cells. It belongs to the class of bacteria that are responsible for most common bacterial infections in human [2]. It is adapted to the acidic gastric environment for survival. It is also indigenous to the worldwide human population. It was first isolated by Marshall and Warren in 1984 [3, 4, 5]. Prolonged infection of the organism can be transformed into a chronic infection that causes severe gastric diseases such as duodenal ulcer, gastric ulcer, gastric lymphonema and cancer [6, 7]. Nonchronic infection of the bacteria is usually asymptomatic. There is usually no development of clinical disease observed in the infected person. The prevalence of infection is also guided by the variations in geographical conditions, age, race, and socioeconomic status of the infected persons [8, 9, 10]. A person having bacterial infection at an early age is more prone to develop a chronic infection [11, 12, 13]. H. pylori infection in developing countries is higher in comparison to the developed countries. The reason behind this may be poor hygiene practices in the developing countries [14].
The H. pylori genome was first sequenced in 1997 [5]. The genome of H. pylori 26695 strain (NC_000915.1) contains 1,555 coding genes and 65 pseudogenes. The GC content of the genome is 38.9%. The coding genes in the genome encode 1,445 proteins, seven rRNAs, and 36 tRNAs. The genome contains 340 predicted gene products characterized as hypothetical proteins (HPs).
In this study, we have analyzed the sequences of all the HPs from H. pylori to assign probable functions. The objective is to identify putative virulence proteins in the proteome that help in pathogenesis. We have used an established protocol [15, 16] for the function prediction of the HPs that comprises leading bioinformatics tools and databases [17, 18, 19]. The analysis goes in a systematic way of predicting physicochemical properties of the proteins using ProtParam. Then, subcellular localization using different programs is carried out to assist the function prediction. Identification of transmembrane helices (TMHs) in the HPs to find out membrane protein is carried out using TMHMM and HMMTOP. We have analyzed the HPs for similarity searching using Basic Local Alignment Search Tool (BLAST). Protein-protein interaction is helpful in assessing the function of novel proteins. We have used Search Tool for the Retrieval of Interacting Genes (STRING) database for predicting protein-protein interaction networks for the HPs. The classification of the HPs is done using CATH, Structural Classification of Proteins (SCOP), Pfam, SVMProt, and Protein Analysis through Evolutionary Relationships (PANTHER) database. Conserved domain discovery and motif search in the HPs are carried out using Conserved Domain Architecture Retrieval Tool (CDART), Simple Modular Architecture Research Tool (SMART), InterProScan, and Motif, respectively. We have made final predictions on the basis of a consensus approach [20, 21, 22]. The putative function predicted by four or more programs for an HP is considered the probable function of that HP with high precision and high confidence [17, 23]. Finally, we have successfully assigned putative functions to 104 HPs out of 340 HPs with high precision. Furthermore, we have classified proteins on the basis of their involvement in the various biological process and predicted molecular functions into diverse functional groups such as enzymes, binding proteins, transporters, and proteins involved in cellular processes and into the proteins exhibiting miscellaneous functions.

Methods

Data abstraction

In this study, the primary source of genome data is National Center for Biotechnology Information (NCBI) genome database. We extracted preliminary information using "Helicobacter pylori" string that redirects to the genomewide project report of H. pylori genome. We selected H. pylori 26695 strain from the database with the Accession Code RefSeq NC_000915.1. The genome contains 1,555 genes coding for 1,445 proteins. We then extracted the hypothetical proteins from the pool of 1,445 proteins. We used Uniprot for retrieving Uniprot IDs and fasta sequences of the HPs using their Protein Product IDs (e.g., NP_206816.1). Fasta sequences of the HPs retrieved from Uniprot were used for further analysis.

Physicochemical parameterization

Physicochemical properties of the proteins help in deducing the biochemical characteristics of the proteins and functional characterization. We used Expassy's ProtParam [24] server for estimation of physicochemical parameters of the HPs. ProtParam server is equipped with modules that are capable of predicting an array of physicochemical properties using predefined formulas and experimental inferences. We predicted relative molecular weight, theoretical pI, extinction coefficient, instability index, aliphatic index, and grand average of the hydropathicity of the HPs using ProtParam. All these properties help in identifying the probable function of the proteins. Data for physicochemical parameterization are listed in Supplementary Table 1.

Sub-cellular localization

The function of a protein is very well influenced by its location in the cellular space. For instance, proteins of the exoproteomic pool and secretory proteins often play an essential role in virulence related activities such as adherence to host cells. We used an array of tools to carry out the subcellular localization of the HPs. We used PSORTb [25], PSLpred [26], and Cello [27] to predict the location of HPs in the cell. These predictors use experimental data from known proteins to make predictions for query proteins using their fasta sequences. They predict the possible occurrence of protein in diverse cellular or extracellular localities such as cytoplasm, periplasm, inner membrane, outer membrane, or extracellular space. To predict signal peptides in the HPs, we used SignalP [28] prediction platform for the existence of signal peptides in the HPs, which is a characteristic feature of membrane-bound proteins. SecretomeP [29] server was used to find out nonclassical secretory proteins among the HPs. Prediction of TMHs in the proteins helps in the identification of membranous proteins. We used HMMTOP [30] and TMMHMM [31] for this purpose. Both these programs use Hidden Markov Model (HMM) profiles of training data set to predict TMHs in query sequences. The supplementary data are given in Supplementary Table 2.

Identification of virulence proteins

The present work put stress upon the identification of potential virulence proteins in the pool of HPs. Pathogenic bacteria contain a range of virulence proteins in their pathogenesis machinery. There are adhesins, exotoxins, endotoxins, and secretion systems, etc., that comprise the virulence moiety of pathogenic bacteria. We used VirulentPred [32] and VICMpred [33] for the identification of virulence factors among the HPs. Both these tools are Support Vector Machine (SVM) based using 5-fold cross-validation processes to validate the results. VirulentPred uses the strategy of two-way predictions, i.e., non-Virulent or Virulent whereas VICMpred categorizes proteins into four classes namely proteins involved in cellular processes, metabolism protein, information molecule, and virulence factors. It has a training set of 670 proteins from Gram-negative bacteria including 70 known virulence factors. Information for virulence factors analysis is provided in Supplementary Table 3.

Homology and function prediction

The assertion of homology between proteins derived on the basis of sequence similarity provides insights into the functional properties of an unknown protein showing similarity with a protein of known function. BLAST [34, 35, 36, 37] is a commonly used and most reliable tool for the purpose. Structure and function prediction help to identify novel drug targets which can be further utilized for therapeutic intervention [38, 39, 40, 41, 42, 43, 44, 45, 46]. We used blastp module to search for homologous proteins to the HPs against a database of nonredundant protein sequences. To decrease the redundancy in the results, a threshold was set for the e-value less than 0.0005 and sequence identity more than 30%. SMART [47] was used for the function prediction. It uses information about domain architecture from known proteins and provides functional annotation of query sequences. Function prediction based on motif discovery was performed using InterProScan [48] and Motif. InterProScan searches the query sequence against Interpro consortium to bring about the function of the proteins using motif information. Motif operates as an interface between user and motif library of known databases. It searches the query sequence against Pfam, TIGRFAM, COG, SMART, PROSITE Patterns, and PROSITE profiles. The user has the facility to choose any of these databases. We also used STRING [49] to predict protein-protein interaction networks for the HPs. It gives functional insights for the HPs based on protein-protein interaction. Information for homology and function prediction is listed in Supplementary Table 4.

Classification and domain assignment

Protein classification and domain assignment using sequence similarity search may give ample evidence for function prediction of the HPs. We have used an array of databases and retrieval tools such as CATH [50], SUPERFAMILY [51], PANTHER [52], Pfam [53], CDART [54], SVMProt [55], and ProtoNet [56] for the classification of the HPs. CATH provides the classification of Protein Data Bank (PDB) protein structure repository. CATH v4.0 release contains 235,858 domains, 2,738 superfamily and 69,058 annotated PDBs. SUPERFAMILY database provides structure and functional annotation of proteins based on HMM using SCOP classification system. PANTHER is another efficient protein classification database based on HMM profiles. PANTHER provides a multi-way classification of proteins on the basis of family and subfamily, molecular function, involvement in a biological process, and association with a pathway in any cellular process. It reduces the risk of redundancy by applying strict HMM scoring strategy. We also used Pfam for the classification of HPs. Pfam is a database of protein families with representative multiple sequence alignments and HMMs for each family. SVMProt was also used for functional classification of the HPs. It is a SVM based classification software trained with the dataset of about 54 functional families of protein. We performed cluster-based classification of the HPs using ProtoNet. It gives a hierarchical classification of proteins using clusters of proteins showing functional similarity. The information about the classification of the HPs is given in Supplementary Table 5.

Results

Sequences of 340 HPs from H. pylori 26695 strain tested with exclusive pipeline developed by our group [23, 57]. We used several tools for the sequence analysis such as, BLAST, CATH, SCOP, CDART, InterProScan, Motif, protein family databases, conserved domain databases, protein cluster database, protein-protein interaction database, and other such analysis tools such as virulence predictors, subcellular localization prediction programs, etc. Data produced by all these methods and prediction programs help us deducing results. We successfully assigned probably functions to 104 HPs with high confidence (Table 1). As mentioned earlier, the basis of the confidence level was consensus based, i.e., the similar function for an HP predicted by four or more programs was considered function for the HP with high confidence and precision. To reduce redundancy and to maintain the reliability of the results, we deliberately omitted the HPs for which functions were predicted with low level and less precision.

Discussion

Classification of the HPs

For the ease of the approach for understanding the probable involvement of these HPs in pathogenesis, we categorized all 104 HPs into various functional groups on the basis of their individual molecular function and their involvement in various biological processes (Fig. 1). We found 27 HPs showing similarities with various enzyme classes like oxidoreductases, hydrolases, transferases, etc. Ten HPs are categorized as transporters, 26 showing features of binding proteins, 23 HPs have predicted to be involved in various cellular and regulatory processes and 18 HPs are listed in the category of proteins showing miscellaneous functions. These HPs are further studied and extensively analyzed using previously available literature and experimental studies.
Enzymes, having catalytic properties, play a substantial role in the life of a living organism to provide biochemical machinery for various cellular and regulatory processes. We found 27 HPs showing similarities experimentally characterized enzymes representatives of enzyme classes. HP O25317 showed similarity with disulfide bond formation protein DsbB. Disulfide bonds provide stability and maturation strength to the protein thus, DsbB has a critical role in the development of substantial protein machinery that may be involved in the metabolic or regulatory pathways [58] of that pathogen, therefore, helping in the pathogenesis. Out of 27 enzymes, five HPs are categorized as transferases. HPs O25589 and O25870 are showing similarity with acetyltransferase family protein and glycosyltransferase family 9 (heptosyltransferase), respectively. Both these HPs are predicted virulent in virulent factors analysis. Glycosyltranferases facilitate the "biosynthesis of disaccharides, oligosaccharides, and polysaccharides" by catalyzing the transfer of sugar moieties [59]. HP O25870 is predicted heptosyltransferase may be a potential drug target. Heptosyltranferase help in the formation of the core region of lipopolysaccharides which constitute the major component outer membrane structure in Gram-negative bacteria [60]. About 60% of all predicted enzymes belong to hydrolases enzyme class and most of them are involved in metabolic pathways. In the predicted hydrolases, there are ATPases, restriction endonucleases, phosphoesterases, etc., that facilitate the processes of transcription, translation, functional group localization, and other such essential activities that help in the development and propagation of the pathogen inside the host. There are four HPs showing similarities with member proteins of lyase enzyme class. HP O25309 is showing similarity with aminodeoxychorismate lyase and is predicted as virulent factor. Aminodeoxychorismate lyase is a class member of pyridoxal-phosphate-binding protein class IV which helps in the biosynthesis of tetrahydrofolate by aminodeoxychorismate to para-aminobenzoate. Tetrahydrofolate is an essential precursor in purine biosynthesis [61].
Transporters have always remained a subject of interest during the process of novel drug discovery against the pathogenic diseases. Transporters, due to their specific evolution making them capable of transporting essential molecules, are involved in a wide range of metabolic pathways and other important cellular processes. H. pylori genome has an ample amount of genes that encode a large number of transporter proteins, mainly ATP-binding cassette (ABC) transporters. In the predicted HPs, we found 10 HPs showing characteristic similarity with transporters. HPs O26020, and O26021 are showing similarity with ABC-2 family transporter proteins. ABC transporters, specific to prokaryotes, are the leading molecules that fulfill the energy requirement of the organism for diverse biological processes [62]. The required amount energy that they provide comes from the hydrolysis of ATP molecules performed by ABC transporters [63] having specifically evolved domains for ATP hydrolysis. We found HP O26042 is showing similarity with ferrichrome iron receptor (fhuA). Iron uptake is believed to be preferential activity in H. pylori for the survival in the host system [64]. fhuA is an outer membrane transport protein which catalyzes the transport of ferrichrome and also acts as a receptor for T5 phages in Escherichia coli and other toxic substances [65]. HP O26042 is also predicted virulent in virulence factors analysis. Thus, it can be considered potential drug target.
Twenty-six HPs are characterized as binding proteins. These proteins are further specified according to their functions as adhensin, DNA-, RNA-, protein-, nucleotide-, metal- and lipid-binding proteins. Some of the representative members of this group are may be known involved in leading cell activities, transcription, translation, and other regulatory processes. In this group, we have identified four HPs showing characteristics of restriction modification proteins, three of which belong to type I and one belong to type II. All these proteins may have an essential role in DNA modification. HP O25934 is showing similarity with type-1 restriction enzyme ecoKI specificity protein (hsdS) and predicted virulent by both VICMpred and VirulentPred. Type-1 restriction enzyme ecoKI specificity protein belongs to the class of S-adenosyl-L-methionine dependent endonucleases that are constituents of bacterial DNA restriction-modification mechanisms, which guards the organism from foreign DNA invasion [66]. We identified HP O25749 showing positive virulence and exhibiting similarity with tetratricopeptide repeat (TPR) protein. TPR is a signature motif of proteins regulating protein-protein interaction and the formation of multiprotein complexes [67]. Proteins with TPR motifs are involved in important biological processes such as cell cycle, protein folding, transcriptional regulation, etc. [68]. Involvement in leading processes makes them liable to be treated as potential drug targets. We found two HPs O25618 and O25619 are showing significant similarity to dynamin like GTPases. The function of dynamin GTPases is well studied in eukaryotes. They are involved in membrane fusion and fission mediated by the hydrolysis of GTP molecules but the exact function of their prokaryotic counterparts, despite the existence of structural data, is not well understood and needs a further probe to straighten out their role in prokaryotes [69].
We have identified 23 HPs may be involved in diverse cellular processes and regulatory mechanisms. Proteins mediating the formation of cell envelope such flagellar biosynthesis proteins, flagellar motility proteins are signature members of this group. Flagella is responsible for bacterial motility in a host environment which helps in the colonization of the pathogen [70]. H. pylori is equipped with "five to seven unipolar" flagella that are protected against gastric acidity due to the presence of a covering sheath formed of phospholipids [71]. There are a relatively higher number of genes in H. pylori that encodes flagellar proteins supporting the fact that motility facilitates the colonization of the pathogen in the host body; thus, their association with bacterial virulence is also subjected to consideration in the course of drug discovery. HP O26095 is showing similarity with flagellar biosynthetic protein flhb that mediates the formation of flagella. It may be a potential drug target. We found HP O25564 similar to flagellar hook-length control protein FliK that controls the length of the flagellar hook during flagellar biosynthesis [72]. In the H. pylori genome, there are seven known genes encoding molecular chaperons. We have identified HP O25894 is showing homology with molecular chaperon. DnaJ, is signature member of the family of molecular chaperons that exhibit a diverse number of molecular functions such ATP binding, metal ion binding, unfolded protein binding and is involved in a number of leading biological processes like protein folding, protein unfolding, DNA replication, and response to heat shock, etc. [73]. The involvement of chaperons in essential cellular processes required for survival and propagation of pathogen make them potential drug targets for the development of effective drugs against pathogenicity.
Though we have categorized HPs in the definite functional classes on the basis of their molecular functions and their involvement in diverse biological processes, but there HPs which exhibit some unique functions or functions are not clearly classified in the available literature. We put those HPs in the group of proteins exhibiting miscellaneous functions. HP O25579 is identified as toxin like outer membrane protein and showing significant virulence in virulence factors analysis. We found HP O25993 similar to lipoprotein with positive virulence. Despite the fact that H. pylori infects the host in the free environment, evidence for adherence to epithelial cells of the gastric tissues of the host are also found [64]. Outer membrane proteins and lipoproteins have an effective role in cell adhesion in H. pylori [5]; thus, they may be taken as strong candidates for drug targets. We identified HP O25713 similar to neuraminyllactose-binding hemagglutinin (NLBH) with substantial virulence. In H. pylori, NLBH, which is also a lipoprotein, has an effective role in adhesion to the gastric epithelium of the host [74]. We identified three characterized genes in the H. pylori genome that encodes NLBH proteins at distant locations. The specific function of NLBH signifies its virulence making it a potential therapeutic target.

Virulence factors

As discussed in the last section, we have performed virulence factors analysis for all the 340 HPs to bring about virulent proteins that play an effective role in the propagation of disease. We preferable selected consensus-based approach for the purpose of taking the results of both predictors VICMpred and VirulentPred as positive. Thus, we found 22 HPs predicted virulent by both these programs (Table 2). While looking for the virulent proteins in the array of 104 predicted HPs, we found 11 HPs showing positive virulence that are mentioned in Table 1. Concrete specification of virulence proteins amongst the predicted functional candidates paves the way for further studies on drug discovery and development in a more focused way. Therefore, results of virulence factors analysis hold significant in the lookups for further study and experimental characterization of predicted HPs.

Subcellular localization

Identification of subcellular location of the protein in a computer based functional analysis is significant because there is a strong relation between the function and location of the protein in cellular space [75, 76]. It also gives insight into the determination of probable drug target or vaccine target among the identified virulent proteins. For the newly assigned 104 HPs, we deduced their relative subcellular locations from the results of subcellular localization prediction discussed earlier on the basis of consensus-based approach. Relative subcellular locations of predicted HPs are given in Table 1. We also classified the predicted HPs based on their subcellular locations (Fig. 2). Associating the results of subcellular localization with those of virulence factors analysis may help in the identification of probable drug or vaccine targets.
In conclusion, computational sequence analysis of HPs in order to find out possible functional clues is an extensive work and need much patience for each gene is individually analyzed with an array of tools and databases. The inferences are drawn with a sensitive approach to discard the possibilities of false-positives. Due to the occurrence of similar looking patterns, prediction software may predict different function for similar HP than that predicted by another tool. Therefore, we have selected a more sensitive consensusbased approach, cross-checking the results of all used programs and then deducing inferences on the basis of majority rule. Majority rule is the criteria taking the function predicted by four or more tools as the probable function of the HP. This way, we have successfully predicted probable functions of 104 HPs with high level confidence. A wide range of HPs showing functional similarities with the proteins those play an essential role in bacterial pathogenesis. The study may pave the way for experimentalists to look forward to the possibilities of in vitro functional characterization of virulent proteins that may be considered potential therapeutic targets in the process of drug discovery.

Acknowledgments

This work is supported by the Indian Council of Medical Research, Government of India (BIC/12(04)/2012) to MIH. The central instrumentation facility of Jamia Millia Islamia is highly acknowledged for providing high-speed server.

Supplementary materials

Supplementary data including five tables can be found with this article online at http://www.genominfo.org/src/sm/gni-14-125-s001.pdf.

Supplementary Table 1

List of predicted physicochemical parameters by Expasy's ProtParam tool of 340 HPs from Helicobacter pylori
gni-14-125-s001.pdf

Supplementary Table 2

List of predicted sub-cellular localization of 340 HPs from Helicobacter pylori
gni-14-125-s002.pdf

Supplementary Table 3

Virulence factors analysis of 340 hypothetical proteins Helicobacter pylori
gni-14-125-s003.pdf

Supplementary Table 4

List of annotated function of 340 hypothetical proteins (HPs) from Helicobacter pylori using BLASTp, STRING, SMART, InterProScan and Motif
gni-14-125-s004.pdf

Supplementary Table 5

List of functionally annotated domain of 340 hypothetical proteins (HPs) from Halicobacter pylori by CATH, SUPERFAMILY, PANTHER, Pfam, CDART, SVMProt, and ProtoNet
gni-14-125-s005.pdf

References

1. Shiota S, Suzuki R, Yamaoka Y. The significance of virulence factors in Helicobacter pylori. J Dig Dis 2013;14:341–349. PMID: 23452293.
crossref pmid pmc
2. Testerman TL, Morris J. Beyond the stomach: an updated view of Helicobacter pylori pathogenesis, diagnosis, and treatment. World J Gastroenterol 2014;20:12781–12808. PMID: 25278678.
crossref pmid pmc
3. Marshall BJ, Warren JR. Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet 1984;1:1311–1315. PMID: 6145023.
crossref pmid
4. Cover TL, Blaser MJ. Helicobacter pylori infection, a paradigm for chronic mucosal inflammation: pathogenesis and implications for eradication and prevention. Adv Intern Med 1996;41:85–117. PMID: 8903587.
pmid
5. Tomb JF, White O, Kerlavage AR, Clayton RA, Sutton GG, Fleischmann RD, et al. The complete genome sequence of the gastric pathogen Helicobacter pylori. Nature 1997;388:539–547. PMID: 9252185.
crossref pmid
6. Yamaoka Y, Graham DY. Helicobacter pylori virulence and cancer pathogenesis. Future Oncol 2014;10:1487–1500. PMID: 25052757.
crossref pmid pmc
7. Cid TP, Fernández MC, Benito Martínez S, Jones NL. Pathogenesis of Helicobacter pylori infection. Helicobacter 2013;18(Suppl 1):12–17. PMID: 24011239.
crossref pmid
8. Nakamura S, Matsumoto T. Helicobacter pylori and gastric mucosa-associated lymphoid tissue lymphoma: recent progress in pathogenesis and management. World J Gastroenterol 2013;19:8181–8187. PMID: 24363507.
crossref pmid pmc
9. Tomoda A, Kamiya S, Suzuki H. Helicobacter pylori and pathogenesis. Biomed Res Int 2015;2015:304768. PMID: 25949992.
pmid pmc
10. Watari J, Chen N, Amenta PS, Fukui H, Oshima T, Tomita T, et al. Helicobacter pylori associated chronic gastritis, clinical syndromes, precancerous lesions, and pathogenesis of gastric cancer development. World J Gastroenterol 2014;20:5461–5473. PMID: 24833876.
crossref pmid pmc
11. de Bernard M, Josenhans C. Pathogenesis of Helicobacter pylori infection. Helicobacter 2014;19(Suppl 1):11–18. PMID: 25167940.
crossref pmid
12. De Falco M, Lucariello A, Iaquinto S, Esposito V, Guerra G, De Luca A. Molecular mechanisms of Helicobacter pylori pathogenesis. J Cell Physiol 2015;230:1702–1707. PMID: 25639461.
crossref pmid
13. Hagiwara T, Mukaisho K, Nakayama T, Hattori T, Sugihara H. Proton pump inhibitors and Helicobacter pylori-associated pathogenesis. Asian Pac J Cancer Prev 2015;16:1315–1319. PMID: 25743791.
crossref pmid pdf
14. Brown LM. Helicobacter pylori: epidemiology and routes of transmission. Epidemiol Rev 2000;22:283–297. PMID: 11218379.
crossref pmid
15. Naqvi AA, Shahbaaz M, Ahmad F, Hassan MI. Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum. PLoS One 2015;10:e0124177. PMID: 25894582.
crossref pmid pmc
16. Naqvi AA, Ahmad F, Hassan MI. Identification of functional candidates amongst hypothetical proteins of Mycobacterium leprae Br4923, a causative agent of leprosy. Genome 2015;58:25–42. PMID: 25985983.
crossref pmid
17. Shahbaaz M, Ahmad F, Hassan MI. Structure-based function analysis of putative conserved proteins with isomerase activity from Haemophilus influenzae. 3 Biotech 2015;5:741–763.
crossref pmid pmc
18. Shahbaaz M, Bisetty K, Ahmad F, Hassan MI. Towards new drug targets? Function prediction of putative proteins of Neisseria meningitidis MC58 and their virulence characterization. OMICS 2015;19:416–434. PMID: 26076386.
crossref pmid pmc
19. Shahbaaz M, Bisetty K, Ahmad F, Hassan I. Current advances in the identification and characterization of putative drug and vaccine targets in the bacterial genomes. Curr Top Med Chem 2016;16:1040–1069. PMID: 26303422.
crossref pmid
20. Kumar K, Prakash A, Islam A, Ahmad F, Hassan MI. Identification of functional candidates amongst hypothetical proteins of Neisseria gonorrhoeae. Lett Drug Des Discov 2016;13:451–464.
crossref
21. Kumar K, Prakash A, Anjum F, Islam A, Ahmad F, Hassan MI. Structure-based functional annotation of hypothetical proteins from Candida dubliniensis: a quest for potential drug targets. 3 Biotech 2015;5:561–576.
crossref pmid pmc
22. Kumar K, Prakash A, Tasleem M, Islam A, Ahmad F, Hassan MI. Functional annotation of putative hypothetical proteins from Candida dubliniensis. Gene 2014;543:93–100. PMID: 24704023.
crossref pmid
23. Shahbaaz M, Hassan MI, Ahmad F. Functional annotation of conserved hypothetical proteins from Haemophilus influenzae Rd KW20. PLoS One 2013;8:e84263. PMID: 24391926.
crossref pmid pmc
24. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784–3788. PMID: 12824418.
crossref pmid pmc
25. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26:1608–1615. PMID: 20472543.
crossref pmid pmc
26. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522–2524. PMID: 15699023.
crossref pmid
27. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004;13:1402–1406. PMID: 15096640.
crossref pmid pmc
28. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 2011;8:785–786. PMID: 21959131.
crossref pmid
29. Bendtsen JD, Kiemer L, Fausbøll A, Brunak S. Non-classical protein secretion in bacteria. BMC Microbiol 2005;5:58. PMID: 16212653.
crossref pmid pmc
30. Tusnády GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics 2001;17:849–850. PMID: 11590105.
crossref pmid
31. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001;305:567–580. PMID: 11152613.
crossref pmid
32. Garg A, Gupta D. VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 2008;9:62. PMID: 18226234.
crossref pmid pmc
33. Saha S, Raghava GP. VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics Proteomics Bioinformatics 2006;4:42–47. PMID: 16689701.
crossref pmid pmc
34. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403–410. PMID: 2231712.
crossref pmid
35. Khan FI, Shahbaaz M, Bisetty K, Waheed A, Sly WS, Ahmad F, et al. Large scale analysis of the mutational landscape in β-glucuronidase: a major player of mucopolysaccharidosis type VII. Gene 2016;576(1 Pt 1):36–44. PMID: 26415878.
crossref pmid
36. Shahbaaz M, Bisetty K, Ahmad F, Hassan MI. In silico approaches for the identification of virulence candidates amongst hypothetical proteins of Mycoplasma pneumoniae 309. Comput Biol Chem 2015;59(Pt A):67–80.
crossref
37. Zaidi S, Hassan MI, Islam A, Ahmad F. The role of key residues in structure, function, and stability of cytochrome-c. Cell Mol Life Sci 2014;71:229–255. PMID: 23615770.
crossref pmid
38. Devika NT, Amresh P, Hassan MI, Ali BM. Molecular modeling and simulation of the human eNOS reductase domain, an enzyme involved in the release of vascular nitric oxide. J Mol Model 2014;20:2470. PMID: 25287765.
crossref pmid
39. Hassan MI. Editorial. Recent advances in the structure-based drug design and discovery. Curr Top Med Chem 2016;16:899–900. PMID: 26381417.
crossref pmid
40. Hassan MI, Bilgrami S, Kumar V, Singh N, Yadav S, Kaur P, et al. Crystal structure of the novel complex formed between zinc alpha2-glycoprotein (ZAG) and prolactin-inducible protein (PIP) from human seminal plasma. J Mol Biol 2008;384:663–672. PMID: 18930737.
crossref pmid
41. Hassan MI, Kumar V, Singh TP, Yadav S. Structural model of human PSA: a target for prostate cancer therapy. Chem Biol Drug Des 2007;70:261–267. PMID: 17718721.
crossref pmid
42. Hassan MI, Kumar V, Somvanshi RK, Dey S, Singh TP, Yadav S. Structure-guided design of peptidic ligand for human prostate specific antigen. J Pept Sci 2007;13:849–855. PMID: 17890654.
crossref pmid
43. Hassan MI, Waheed A, Grubb JH, Klei HE, Korolev S, Sly WS. High resolution crystal structure of human β-glucuronidase reveals structural basis of lysosome targeting. PLoS One 2013;8:e79687. PMID: 24260279.
crossref pmid pmc
44. Hoda N, Naz H, Jameel E, Shandilya A, Dey S, Hassan MI, et al. Curcumin specifically binds to the human calcium-calmodulin-dependent protein kinase IV: fluorescence and molecular dynamics simulation studies. J Biomol Struct Dyn 2016;34:572–584. PMID: 25929263.
crossref pmid
45. Khan FI, Aamir M, Wei DQ, Ahmad F, Hassan MI. Molecular mechanism of Ras-related protein Rab-5A and effect of mutations in the catalytically active phosphate-binding loop. J Biomol Struct Dyn 2016;1–14.
crossref
46. Naz F, Shahbaaz M, Khan S, Bisetty K, Islam A, Ahmad F, et al. PKR-inhibitor binds efficiently with human microtubule affinity-regulating kinase 4. J Mol Graph Model 2015;62:245–252. PMID: 26519933.
crossref pmid
47. Letunic I, Doerks T, Bork P. SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res 2012;40:D302–D305. PMID: 22053084.
crossref pmid pmc
48. Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 2014;30:1236–1240. PMID: 24451626.
crossref pmid pmc
49. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013;41:D808–D815. PMID: 23203871.
crossref pmid pmc
50. Sillitoe I, Cuff AL, Dessailly BH, Dawson NL, Furnham N, Lee D, et al. New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures. Nucleic Acids Res 2013;41:D490–D498. PMID: 23203873.
crossref pmid pmc
51. Lo Conte L, Ailey B, Hubbard TJ, Brenner SE, Murzin AG, Chothia C. SCOP: a structural classification of proteins database. Nucleic Acids Res 2000;28:257–259. PMID: 10592240.
crossref pmid pmc
52. Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res 2013;41:D377–D386. PMID: 23193289.
crossref pmid pmc
53. Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, et al. The Pfam protein families database. Nucleic Acids Res 2004;32:D138–D141. PMID: 14681378.
crossref pmid pmc
54. Geer LY, Domrachev M, Lipman DJ, Bryant SH. CDART: protein homology by domain architecture. Genome Res 2002;12:1619–1623. PMID: 12368255.
crossref pmid pmc
55. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 2003;31:3692–3697. PMID: 12824396.
crossref pmid pmc
56. Rappoport N, Karsenty S, Stern A, Linial N, Linial M. ProtoNet 6.0: organizing 10 million protein sequences in a compact hierarchical family tree. Nucleic Acids Res 2012;40:D313–D320. PMID: 22121228.
crossref pmid pmc
57. Khan S, Shahbaaz M, Bisetty K, Ahmad F, Hassan MI. Classification and functional analyses of putative conserved proteins from Chlamydophila pneumoniae CWL029. Interdiscip Sci 2015 Dec 9 [Epub]. PMID: 10.1007/s12539-015-0134-7.

58. Kadokura H, Katzen F, Beckwith J. Protein disulfide bond formation in prokaryotes. Annu Rev Biochem 2003;72:111–135. PMID: 12524212.
crossref pmid
59. Campbell JA, Davies GJ, Bulone V, Henrissat B. A classification of nucleotide-diphospho-sugar glycosyltransferases based on amino acid sequence similarities. Biochem J 1997;326(Pt 3):929–939. PMID: 9334165.
crossref pmid pmc
60. Gronow S, Brabetz W, Brade H. Comparative functional characterization in vitro of heptosyltransferase I (WaaC) and II (WaaF) from Escherichia coli. Eur J Biochem 2000;267:6602–6611. PMID: 11054112.
crossref pmid
61. Green JM, Merkel WK, Nichols BP. Characterization and sequence of Escherichia coli pabC, the gene encoding aminodeoxychorismate lyase, a pyridoxal phosphate-containing enzyme. J Bacteriol 1992;174:5317–5323. PMID: 1644759.
crossref pmid pmc
62. Higgins CF. ABC transporters: physiology, structure and mechanism: an overview. Res Microbiol 2001;152:205–210. PMID: 11421269.
crossref pmid
63. Schneider E, Hunke S. ATP-binding-cassette (ABC) transport systems: functional and structural aspects of the ATP-hydrolyzing subunits/domains. FEMS Microbiol Rev 1998;22:1–20. PMID: 9640644.
crossref pmid
64. Labigne A, de Reuse H. Determinants of Helicobacter pylori pathogenicity. Infect Agents Dis 1996;5:191–202. PMID: 8884364.
pmid
65. Bonhivers M, Ghazi A, Boulanger P, Letellier L. FhuA, a transporter of the Escherichia coli outer membrane, is converted into a channel upon binding of bacteriophage T5. EMBO J 1996;15:1850–1856. PMID: 8617231.
pmid pmc
66. Kisiela M, Skarka A, Ebert B, Maser E. Hydroxysteroid dehydrogenases (HSDs) in bacteria: a bioinformatic perspective. J Steroid Biochem Mol Biol 2012;129:31–46. PMID: 21884790.
crossref pmid
67. Cerveny L, Straskova A, Dankova V, Hartlova A, Ceckova M, Staud F, et al. Tetratricopeptide repeat motifs in the world of bacterial pathogens: role in virulence mechanisms. Infect Immun 2013;81:629–635. PMID: 23264049.
crossref pmid pmc
68. Goebl M, Yanagida M. The TPR snap helix: a novel protein repeat motif from mitosis to transcription. Trends Biochem Sci 1991;16:173–177. PMID: 1882418.
crossref pmid
69. Praefcke GJ, McMahon HT. The dynamin superfamily: universal membrane tubulation and fission molecules? Nat Rev Mol Cell Biol 2004;5:133–147. PMID: 15040446.
crossref pmid
70. Marais A, Mendz GL, Hazell SL, Mégraud F. Metabolism and genetics of Helicobacter pylori: the genome era. Microbiol Mol Biol Rev 1999;63:642–674. PMID: 10477311.
pmid pmc
71. Geis G, Suerbaum S, Forsthoff B, Leying H, Opferkuch W. Ultrastructure and biochemical studies of the flagellar sheath of Helicobacter pylori. J Med Microbiol 1993;38:371–377. PMID: 8487294.
crossref pmid
72. Kawagishi I, Homma M, Williams AW, Macnab RM. Characterization of the flagellar hook length control protein fliK of Salmonella typhimurium and Escherichia coli. J Bacteriol 1996;178:2954–2959. PMID: 8631687.
crossref pmid pmc
73. Bukau B. Regulation of the Escherichia coli heat-shock response. Mol Microbiol 1993;9:671–680. PMID: 7901731.
crossref pmid
74. Chaturvedi G, Tewari R, Mrigank , Agnihotri N, Vishwakarma RA, Ganguly NK. Inhibition of Helicobacter pylori adherence by a peptide derived from neuraminyl lactose binding adhesin. Mol Cell Biochem 2001;228:83–89. PMID: 11855744.
crossref pmid
75. Scott MS, Calafell SJ, Thomas DY, Hallett MT. Refining protein subcellular localization. PLoS Comput Biol 2005;1:e66. PMID: 16322766.
crossref pmid pmc
76. Bork P, Dandekar T, Diaz-Lazcoz Y, Eisenhaber F, Huynen M, Yuan Y. Predicting function: from genes to genomes and back. J Mol Biol 1998;283:707–725. PMID: 9790834.
crossref pmid
Fig. 1

Classification of hypothetical proteins into enzymes (n=27), transporters (n=10), binding proteins (n=26), cellular processes/regulatory proteins (n=23) and miscellaneous functions (n=18).

gni-14-125-g001
Fig. 2

Classification of HPs on the basis of subcellular localization.

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Table 1.

List of 104 HPs with predicted functions from Helicobacter pylori

No. Uniprot ID Function Molecular weight (Da) Theoretical PI Subcellular localization
1 O24860 TrbC/VIRB2 family protein 10,525.7 8.98 Inner membrane
2 P56066 ATP-dependent Clp protease (ClpS) 10,344.0 5.61 Cytoplasmic
3 O24894a His-Me finger endonucleases-like superfamily 49,556.6 8.64 Cytoplasmic
4 O24904 Cell wall assembly and cell proliferation coordinating protein, KNR4-like 16,102.3 5.06 Cytoplasmic
5 O24914 NLP/P60 family protein-like domain 52,340.1 9.24 Inner membrane
6 O24934 Class II Aldolase and adducin N-terminal domain 27,143.3 8.62 Cytoplasmic
7 P56080 Radical SAM superfamily 4Fe-4S single cluster domain 34,405.4 8.81 Cytoplasmic
8 O24951 Cysteine-rich domain 27,491.8 6.94 Cytoplasmic
9 O24963 VitK2_biosynth family 32,948.5 8.56 Cytoplasmic
10 O24965 AMIN domain protein 23,248.0 9.03 Cytoplasmic
11 O24976 Chemotaxis phosphatase CheZ 28,621.8 4.63 Cytoplasmic
12 P56117 Phospholipase D/nuclease superfamily 58,287.1 9.39 Cytoplasmic
13 O25010 Ribbon-helix-helix protein, copG family 8,598.8 9.52 Cytoplasmic
14 O25022 Cytochrome c-like domain 14,438.9 8.44 Cytoplasmic
15 O25038 MgtE intracellular N domain 24,814.9 8.40 Cytoplasmic
16 P56132 Putative zinc- or iron-chelating domain 15,175.6 8.33 Cytoplasmic
17 O25053 Indole-3-glycerol phosphate synthase 21,035.3 5.34 Cytoplasmic
18 O25058 TrkA-C domain 56,163.3 8.93 Cytoplasmic
19 O25075 Alginate lyase-like domain 37,588.9 8.64 Extracellular
20 O25076 YceI-like domain protein 20,384.8 9.32 Periplasmic
21 O25146 Sporulation/cell division region 29,031.0 9.33 Periplasmic
22 O25155 Calcineurin-like phosphoesterase 29,506.3 8.72 Cytoplasmic
23 O25156 Alanine racemase, N-terminal domain 25,011.0 7.68 Cytoplasmic
24 O25174 Thioesterase/thiol ester dehydrase-isomerase 16,129.6 5.24 Cytoplasmic
25 O25177 DHH phosphoesterase 47,547.4 6.23 Cytoplasmic
26 O25178 Von Willebrand factor type A (vWA) domain 21,136.3 8.44 Cytoplasmic
27 O25192 Toprim-like 58,853.9 8.98 Cytoplasmic
28 O25195 ATPase AAA 41,437.3 5.36 Cytoplasmic
29 O25201 AAA domain 27,447.4 8.31 Cytoplasmic
30 O25213 Tellurite resistance protein TerB 29,846.0 4.87 Cytoplasmic
31 O25255 L,D-transpeptidase catalytic domain 38,618.3 9.22 Outer membrane
32 O25292 Iron-sulfur cluster-binding domain 29,345.4 9.21 Cytoplasmic
33 O25301 Sulfatase 77,616.6 9.16 Inner membrane
34 O25309a Aminodeoxychorismate lyase 37,615.9 9.32 Cytoplasmic
35 O25317 Disulfide bond formation protein DsbB 55,481.2 8.46 Inner membrane
36 O25373 SurA N-terminal domain 47,633.3 8.93 Cytoplasmic
37 O25408 Transcriptional regulatory protein tyrr 43,414.4 6.31 Cytoplasmic
38 O25431 GTP-binding protein, HSR1-related 66,056.1 5.65 Cytoplasmic
39 O25442 Fibronectin type-III domain 48,082.6 9.18 Outer membrane
40 O25450 Molybdopterin biosynthesis protein (MoeB) 23,847.4 8.97 Cytoplasmic
41 O25456 5-Formyltetrahydrofolate cyclo-ligase family 23,647.9 9.98 Cytoplasmic
42 O25468 VitK2_biosynth/menaquinone biosynthesis 26,738.5 9.90 Cytoplasmic
43 O25510 Outer membrane protein transport protein 63,653.5 9.50 Outer membrane
44 O25520 Type I restriction endonuclease subunit S 11,289.9 6.73 Extracellular
45 O25562 Cupin, RmlC-type 11,030.0 6.19 Cytoplasmic
46 O25564 Flagellar hook-length control protein FliK 58,160.8 9.14 Extracellular
47 O25576 DHBP synthase RibB-like alpha/beta domain 16,136.6 10.13 Outer membrane
48 O25579a Toxin-like outer membrane protein 274,562.7 5.78 Extracellular
49 O25589a Acetyltransferase family protein 18,418.3 5.84 Cytoplasmic
50 O25616 50S ribosome-binding GTPase 51,795.4 5.01 Cytoplasmic
51 O25618 Dynamin family protein/GTPase 50,173.9 6.61 Cytoplasmic
52 O25619 Dynamin family protein/GTPase 62,576.6 5.43 Cytoplasmic
53 O25624 Outer membrane efflux protein 47,710.0 8.24 Cytoplasmic
54 O25630 Peptidase M50 family protein 11,409.5 8.98 Inner membrane
55 O25642 Nucleotidyl transferase 30,853.8 5.64 Outer membrane
56 O25704 Prokaryotic metallothionein family protein 10,871.9 9.42 Cytoplasmic
57 O25708 Zn-dependent exopeptidases 50,233.2 9.00 Cytoplasmic
58 O25713a Neuraminyllactose-binding hemagglutinin precursor (NLBH) 23,736.8 9.52 Inner membrane
59 O25721 PD-(D/E)XK nuclease superfamily 90,792.8 5.54 Cytoplasmic
60 O25747 Anti-sigma-28 factor, FlgM 8,598.9 8.96 Cytoplasmic
61 O25749 Tetratricopeptide repeat containing protein 38,377.7 9.37 Outer membrane
62 O25761 AAA domain protein 88,752.5 5.56 Cytoplasmic
63 O25768 KH domain RNA binding protein 12,710.6 7.80 Periplasmic
64 O25803 Flagellar motility protein 10,282.0 9.93 Periplasmic
65 O25808 Haloacid dehalogenase-like hydrolase 25,560.1 5.91 Cytoplasmic
66 O25816 RDD family proetin 18,699.6 8.20 Inner membrane
67 O25843 SH3 domain protein 21,396.7 9.06 Cytoplasmic
68 O25848 RDD family proetin 17,843.4 10.07 Inner membrane
69 O25864a Tetratricopeptide repeat containing protein 92,524.5 8.90 Cytoplasmic
70 O25866 Telomere-length maintenance and DNA damage repair 10,736.1 6.09 Cytoplasmic
71 O25870a Glycosyltransferase family 9 (heptosyltransferase) 56,944.3 9.34 Cytoplasmic
72 O25872 HAD superfamily, subfamily IIIB (acid phosphatase) 26,297.4 9.30 Outer membrane
73 O25873 YceI-like domain protein 20,614.7 9.20 Periplasmic
74 O25884 YtkA-like family protein 13,829.3 9.62 Periplasmic
75 O25886a HlyD-like secretion protein 38,611.7 8.94 Outer membrane
76 O25888 Branched-chain amino acid transport protein (AzlD) 13,303.2 9.51 Inner membrane
77 O25892 NYN domain 26,487.8 9.28 Cytoplasmic
78 O25894 DnaJ molecular chaperone homology domain 29,728.8 9.14 Cytoplasmic
79 O25906 Restriction endonuclease-like 34,798.8 7.03 Cytoplasmic
80 O25930 Outer membrane protein assembly factor BamD 26,256.4 9.32 Cytoplasmic
81 O25933 DNA/RNA non-specific endonuclease 15,475.4 8.76 Cytoplasmic
82 O25934a Type-1 restriction enzyme ecoki specificity protein 18,193.6 10.04 Cytoplasmic
83 O25942 Fibronectin-binding protein A N-terminus (FbpA) 51,095.6 9.34 Cytoplasmic
84 O25960 Iojap superfamily ortholog 13,011.9 4.84 Cytoplasmic
85 O25966 S4 domain protein 9,385.0 9.55 Cytoplasmic
86 O25990 Jag N-terminus 23,434.2 9.83 Cytoplasmic
87 O25993a LPP20 lipoprotein 33,870.7 9.31 Outer membrane
88 O25998 Heat shock protein HSLJ 20,480.2 6.54 Cytoplasmic
89 O26000 Mce related family protein 30,489.2 8.34 Outer membrane
90 O26006 Type I restriction modification DNA specificity domain 45,084.6 8.14 Outer membrane
91 O26007 Type I restriction modification DNA specificity domain 77,049.7 8.18 Outer membrane
92 O26014 TPR repeat family protein 96,653.8 6.18 Cytoplasmic
93 O26015 Carbon-nitrogen hydrolase 30,759.2 5.20 Cytoplasmic
94 O26020 ABC-2 family transporter protein 42,545.2 7.16 Inner membrane
95 O26021 ABC-2 family transporter protein 41,089.8 8.80 Inner membrane
96 O26022 Outer membrane efflux proteins (OEP) 57,011.9 9.16 Outer membrane
97 O26025 NIF system FeS cluster assembly, NifU, C-terminal 10,120.9 6.57 Cytoplasmic
98 O26035 Riboflavin biosynthesis protein (ribG) 39,025.2 8.51 Cytoplasmic
99 O26042a Ferrichrome iron receptor-related 97,384.6 9.05 Outer membrane
100 O26046 Type IIS restriction enzyme R and M protein (ECO57IR) 149,716.0 7.14 Cytoplasmic
101 O26058 Purine nucleoside phosphorylase (PunB) 20,200.3 5.42 Cytoplasmic
102 O26095 Flagellar biosynthetic protein flhb 9,981.6 5.26 Cytoplasmic
103 O26100 PAP2 family protein 24,548.7 9.62 Inner membrane
104 O26107 Ubiquinol-cytochrome C chaperone 28,417.6 5.45 Cytoplasmic

a Hypothetical proteins (HPs) predicted virulent in virulence factors analysis.

Table 2.

List of predicted virulent proteins from Helicobacter pylori

No. Uniprot ID VirulentPred VICMPred
1 O24863 Yes Yes
2 O24894 Yes Yes
3 O24909 Yes Yes
4 O25085 Yes Yes
5 O34410 Yes Yes
6 O25309 Yes Yes
7 O25457 Yes Yes
8 O25579 Yes Yes
9 O25589 Yes Yes
10 O25601 Yes Yes
11 O25713 Yes Yes
12 O34410 Yes Yes
13 K4NT00 Yes Yes
14 O25864 Yes Yes
15 O25870 Yes Yes
16 O25886 Yes Yes
17 O25934 Yes Yes
18 O25979 Yes Yes
19 O25993 Yes Yes
20 O26042 Yes Yes
21 K4NEW8 Yes Yes


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