BACKGROUND
in silico prediction has been proved to be of great significance among various disciplines of life sciences including biomedical research
<cit> .
the conventional vaccine development methods are time consuming as they require cultivation of pathogenic microorganisms in laboratory conditions and their dissection using microbiological, biochemical and immunological methods in order to identify the components important for immunogenecity.
these methods are ineffective in circumstances where the cultivation of bacteria is difficult or impossible.
the other limitations arise when the expression of protective antigens is less or absent in in vitro conditions compared to in vivo diseased conditions
<cit> .
in comparison to conventional live attenuated vaccines, subunit vaccines are more reliable as far as safety is concerned
<cit> .
vaccine candidate identification is an essential and important component in subunit vaccine development.
the integration of genomics in vaccine research  is expected to revolutionize novel vaccine candidate identification
<cit> .
computational approach, especially reverse vaccinology  method assists the identification of vaccine candidates from genomes without culturing microorganisms and thus facilitates the subunit vaccine development.
these methods are useful in reducing time, cost and number of wet lab experiments
<cit> .
the rv is a computational pipeline for identification of vaccine candidates against microorganisms from their genome sequences.
thus, all proteins of an organism can be screened computationally for their vaccine potential.
significant success of this principle for vaccine development had already been demonstrated in several pathogens, including neisseria meningitides <cit> , helicobacter pylori <cit> , streptococcus pneumoniae <cit> , porphyromonas gingivalis <cit> , chlamydia pneumoniae <cit>  and bacillus anthracis <cit> .
the relevance of this method was recognized when vaccines developed from capsular polysaccharides of n. meningitides b failed due to cross reactivity against human tissue
<cit> .
application of rv techniques for pvc identification and then in vivo testing led to the development of licensed broad specificity protein vaccine, 5cvmb, against n. meningitides.
this vaccine contains  <dig> protein antigen components, gna <dig>  gna <dig>  gna <dig>  gna <dig> and nada, which were primarily discovered by rv methods
<cit> .
however in earlier rv techniques, protein localization  was used as the main criterion for identification of pvcs.
as a result, a large number of proteins were required to be expressed, purified and tested to obtain few vaccine candidates leading to enormous loss of cost and time.
on the other hand, identifying immunogenic proteins  by using epitope prediction software and web servers have several limitations.
comparative studies have shown that b-cell epitopes  and class-ii mhc-binding t-cell epitopes  prediction methods are not accurate
<cit> .
over-prediction, inability in exact position prediction of epitopes and absence of success in identifying known epitopes in proteins are major concerns in vaccine candidate identification.
until now the available pvcs prediction software and web servers have not been much effective for identification of vaccine candidates from genomes for vaccine design.
vaxijen server, based on discriminant analysis and partial least square  methods, was developed by using datasets of known  protective antigenic and non-antigenic  proteins to predict pvcs
<cit> .
surprisingly, it predicts more than half of proteins from a given bacterial proteome as protective antigens with default parameters making its usage almost impractical.
further, existing software and web servers predict different proteins as vaccine candidates from same proteome sequences.
for example, different proteins were predicted from s. pneumoniae proteome by vaxijen
<cit>  server and new enhanced reverse vaccinology environment
<cit>  software.
from  <dig> proteins of s. pneumoniae, vaxijen  and nerve predicted  <dig> and  <dig> as pvcs, respectively while only  <dig> proteins were common between them.
none of the common pvcs matched with  <dig> known vaccine candidates in s. pneumoniae .
this outcome complicates decision process regarding which tool’s output should be taken for experimental testing to identify vaccine candidates.
the method used in nerve
<cit>  and vaxign
<cit>  tools presumed that extracellular proteins having adhesin-likeliness are potential vaccine candidates.
although adhesin-likeliness of a protein is an important criterion, it should not be considered as the only one because several non-adhesin functional classes of proteins  are also involved in host-pathogen interactions or pathogenesis and many of them are known to be antigenic
<cit> .
it has been suggested that targeting host-pathogen interactions and disease processes at molecular level can be used for novel vaccine discovery
<cit> .
in several cases, the immune responses against these non-adhesins were known to provide protection against microbial infection
<cit> .
invasin, porin, flagellin and toxin have roles in host cell invasion
<cit> ; transportation activity is associated with pathogenesis and virulence
<cit> ; chemotaxis, adhesion and colonization are making pathogenic bacteria to be virulent
<cit> ; and host cell death
<cit> , respectively.
bacterial fibronectin-binding proteins  target host fibronectin for adhesion and colonization
<cit> ; transferrin-binding proteins  are used by bacteria to obtain iron directly from host transferrins
<cit> ; and penicillin-binding proteins  are involved in peptidoglycan biosynthesis to maintain cell wall structure and protection
<cit> .
the solute binding proteins  are used to capture nutrients like iron to overcome the environment devoid of free nutrients within the host
<cit> .
choline-binding proteins  in some bacteria perform adhesin-like function
<cit> .
functional classes of proteins involved in virulence
<cit> , invasion
<cit>  and colonization
<cit> ; porins
<cit>  and flagellin
<cit> ; and binding proteins of choline
<cit> , penicillin
<cit> , transferrin
<cit> , fibronectin
<cit>  and solute
<cit>  are important in host-pathogen interactions and pathogenesis.
since many proteins from these functional classes provide protective immune responses against microbial infection
<cit> , the knowledge of host-pathogen interactions related to bacterial pathogenesis could be used to rationalize and improve vaccine candidate prediction.
a web server, jenner-predict, has been developed which is capable of predicting pvcs from proteome/protein sequences.
it is based on the principle that non-cytosolic proteins having functions  important in host-pathogen interactions and/or pathogenesis are potential vaccine candidates .
it has two broad components: pvcs prediction and analysis of their vaccine potential.
the pvcs prediction is performed in three sequential steps: prediction of subcellular localization, expressibility in laboratory and presence of domains critical in host-pathogen interactions and pathogenesis.
software psortb  <dig>  is used for protein subcellular localization prediction
<cit> .
a protein has high probability of failure to express in experiment
<cit>  when it has more trans-membrane helices.
hmmtop  <dig>
<cit>  software is used for topology prediction and proteins with more than two trans-membrane helices are discarded.
proteins pass through above two filters, and having domains involved in host-pathogen interactions and pathogenesis from functional classes of adhesin, invasin, toxin, porins, colonization, virulence, flagellin, penicillin-binding, choline-binding transferring-binding, fibronectin-binding and solute-binding proteins are selected as vaccine candidates.
standalone pfam sequence search is used for prediction of domains
<cit> .
vaccine potential of pvcs is predicted on the basis of their possible immunogenicity, absence of autoimmunity, and conservation across different pathogenic and non-pathogenic strains of same bacteria.
known bces and tces from immune epitope database
<cit>  are mapped separately on predicted pvcs to know their possible immunogenic region and potential.
this mapping of antigenic determinant  is instrumental in predicting humoral  or cellular  or both immune responses of pvc.
since pvcs specific to pathogenic strains are expected to be involved in virulence
<cit> , therefore conservation of pvcs in different pathogenic strains of same organism is determined to provide more robust vaccine candidates.
the pvcs having homolog in host  are provided by the web server.
such pvcs may produce autoimmunity
<cit>  or less immune response
<cit> .
taking into account above criteria, output of the web server is provided as prioritized pvcs in the result table.
comparison among pvc prediction methods has shown that jenner-predict server’s performance is better.
RESULTS
the web server, jenner-predict, has been developed to predict pvcs from proteome or protein sequences for subunit vaccine development on the basis of domains critical to host-pathogen interactions and pathogenesis.
besides predicting pvcs, it also furnishes information crucial to determine their vaccine capability in terms of immunogenic potential by matching pvcs against iedb epitopes, autoimmunity through matching pvcs with human proteome, and their conservation across different pathogenic and non-pathogenic strains of the organism .
a tutorial explaining how to submit a job as well as user-friendly interpretation of results is available at the web server’s home page.
the web server gives higher priority to pvcs containing more iedb epitope matches as they increase their possibility to be immunogenic.
the pvcs containing exact iedb epitopes match are shown in white background.
the web server also decreases priority of pvcs having human homologs as such pvcs should be discouraged from further vaccine development process.
this prioritization is instrumental in selecting few pvcs for further vaccine development experiments.
the performance of the web server was evaluated against reported vaccine candidates in s. pneumoniae  and e. coli , proteins {both positive  and negative } used for the development of vaxijen server
<cit>  and protective antigens from more than  <dig> bacteria reported in protegen database
<cit> .
pvcs prediction in s. pneumoniae and e. coli
in s. pneumoniae proteome, jenner-predict server predicted  <dig> proteins as vaccine candidates .
as vaxijen server predicts more than half of a proteome as vaccine candidates in any bacteria with default vaxijen probability score,  <dig> , a cut-off of  <dig>  was considered to restrict the number of pvcs so that the performance of this approach can be compared against all other methods.
from s. pneumoniae proteome, our web server predicted  <dig> out of  <dig> known non-cytoplasmic pvcs whereas the software, nerve, and servers, vaxign and vaxijen, predicted only  <dig>   <dig> and  <dig> pvcs, respectively .
as compared to other methods, the pvcs predicted exclusively by jenner-predict server were stk
<cit> , nana
<cit> , and psaa
<cit>  that are having pasta, bnr, and sbp domains, respectively .
other than  <dig> reported vaccine candidates from  <dig> pvcs , our web server predicted  <dig> abc-transporters and solute-binding,  <dig> choline-binding,  <dig> penicillin-binding,  <dig> lysm-containing domain,  <dig> cell wall anchors, etc.
abc-transporter
<cit> , choline-binding
<cit>  and penicillin-binding
<cit>  proteins are known to be potential pvcs in different bacteria including s. pneumoniae.
in e. coli proteome, jenner-predict server predicted  <dig> proteins as vaccine candidates  whereas the nerve and vaxijen predicted more than  <dig> and  <dig> proteins, respectively.
our web server predicted  <dig> out of  <dig> known pvcs whereas software, nerve and servers, vaxign and vaxijen, predicted  <dig>   <dig> and  <dig> pvcs, respectively.
the pvcs missed out by other methods due to being non-adhesins were ompa , btub , tolc  and irea  .
besides  <dig> known protective antigens, the majority of predicted pvcs by our web server were  <dig> bpd transporter proteins,  <dig> solute-binding proteins
<cit>  and  <dig> fimbrial proteins
<cit>  .
prediction of pvcs against protegen database and datasets used in vaxijen server development
the results of our web server for prediction of known protective vaccine candidates from more than  <dig> diverse bacteria reported in protegen database and its comparison against other similar methods has been presented in additional file
6: table s <dig>  our web server predicted  <dig> out of  <dig> protective antigens  from protegen database whereas software, nerve, and servers, vaxign and vaxijen, predicted  <dig>   <dig> and  <dig>  respectively.
the protective antigens  which were only predicted by our method and skipped by others  belong to functional classes of solute binding, toxin, invasin, etc.
.
the jenner-predict server was found to be efficient in discriminating between antigens and non-antigens.
from the  <dig> pas  used in vaxijen server development, nerve, vaxign and vaxijen predicted  <dig>   <dig> and  <dig> proteins, respectively whereas our web server predicted  <dig> pvcs ).
from negative dataset  of  <dig> proteins, the nerve, vaxign and vaxijen methods predicted  <dig>   <dig> and  <dig> proteins to be vaccine candidates, respectively compared to  <dig> proteins  by our web server ).
negative dataset proteins were considered as non-antigens.
but one out of two pvcs predicted by our web server has already been known to be antigenic: fibronectin-attachment protein  provides protective immunity against mycobacterium avium infection
<cit> .
validation of jenner-predict
sensitivity and specificity indices of different pvc prediction methods have been presented in table
<dig>  jenner-predict server’s pvcs prediction accuracy is better at all levels: bacterial proteomes, protegen database and datasets used for vaxijen server development.
unavailability of total number of known vaccine candidates in a proteome prevented us to calculate sensitivity and specificity values for proteome sequences.
the results of pvc prediction from two proteomes by different methods have been provided in table
<dig>  detailed comparison of results w.r.t known vaccine candidates of s. pneumoniae and e. coli by different methods has been provided in additional file
1: table s <dig> and additional file
5: s <dig>  respectively.
on dataset used in development of vaxijen server, sensitivity and specificity of our tool were  <dig>  and  <dig> , respectively whereas comparable methods nerve, vaxign and vaxijen have corresponding values  <dig>  and  <dig> ;  <dig>  and  <dig>  and  <dig>  and  <dig> , respectively.
for the protegen database, only sensitivity was calculated as specificity calculation was not feasible due to lack of negative dataset.
the sensitivities of nerve, vaxign and vaxijen were  <dig> ,  <dig>  and  <dig> , respectively as compared to  <dig>  for jenner-predict server.
* see method section for details.
# values within square bracket indicates total number of proteins vaccine candidates  predicted by respective software/server.
value within parenthesis indicates experimentally known protective vaccine candidates in that organism whereas the values in bold give the number of pvcs predicted by respective software/server from experimentally known protective vaccine candidates in that organism.
$ values indicate the number of pvcs predicted by respective software/server from  <dig> bacterial protective antigens  taken for evaluation.
% values indicate the number of pvcs predicted by respective software/server from  <dig> and  <dig> proteins for positive and negative datasets, respectively.
discussion
the motivation behind developing ‘jenner-predict’ web server is to provide credible vaccine candidates and information regarding their vaccine potential in terms of possible immunogenicity, absence of autoimmunity and conservation so that subunit vaccine development can be accelerated.
the outcome of the web server has substantiated that domains involved in host-pathogen interactions are better criterion for prediction of pvcs than approaches dependent upon only adhesin-likeliness or machine learning.
as pvcs are predicted based on their functions, biologists can assess the importance of a given function in pathogenesis for that organism.
the information regarding the function of pvc could be instrumental for vaccine development.
for example, colonization is crucial in streptococcus pathogenesis and proteins  involved in this process were used as vaccine candidates
<cit> .
most of the earlier rv methods focused on outer membrane or secretory proteins of a proteome to identify pvcs.
pizza et al.
screened proteome sequences of n. meningitis to identify proteins which were probably surface exposed or involved in transportation and obtained  <dig> proteins.
out of them,  <dig> proteins were expressed and experimentally tested for their immunogenic potential.
finally,  <dig> proteins were found to provide protective immunity against n. meningitides <cit> .
similarly, wizemann et al.
searched for motifs related to secretory or surface binding proteins in s. pneumoniae proteome and identified  <dig> proteins containing such motifs.
out of them,  <dig> were expressed and tested for their protective immunity.
finally,  <dig> proteins were found as protective antigens.
similar studies were performed for p. gingivalis <cit>  and c. pneumoniae <cit> .
although proteins providing immunity were identified in these studies but the number of experiments, cost and time requirement were enormous even for identifying pvcs from a particular localization.
on the contrary, jenner-predict server is relying on protein domains involved in host-pathogen interactions for providing reasonably less number of prioritized vaccine candidates from a proteome.
for better validation of vaccine candidates, the user may select few prospective vaccine candidates for experimental testing to verify their protective immunity.
the bce and tce mapping algorithms were developed to identify possible immunogenic region and consequently prediction of immunogenic potential of a protein.
but these methods have drawbacks of over-prediction and even predict epitope in known non-antigenic proteins
<cit> .
currently available antigen or pvc prediction methods were not validated on complete or diverse data.
nerve software was evaluated on its prediction ability of popular vaccine candidates from five bacterial proteomes instead of all known vaccine candidates in those organisms.
similarly, the vaxign server was evaluated against only limited number of known outer membrane protein  vaccine candidates from uropathogenic e. coli <cit> .
further, the vaxijen server was developed on limited data.
even some of the sequences used as the negative data  for web server development were predicted as vaccine candidates  by all other methods ).
our web server predicted two such proteins  as pvcs from the negative dataset sequences.
experimental data confirmed that alanine and proline rich secreted protein  is immunogenic
<cit>  whereas the other protein  is a periplasmic serine or membrane protease  and has already been reported as protective antigen in haemophilus influenzae <cit> .
the other pvcs predicted from negative dataset by nerve, vaxign and vaxijen have no evidence of being immunogenic.
this outcome justifies higher sensitivity of our method.
to provide prospective pvcs of a proteome, the predicted vaccine candidates are prioritized by jenner-predict server.
the pvcs having more iedb epitope matches are ranked higher as such epitope match increases their possibility to be immunogenic.
epitopes identified using ‘hands on’ peptide-by-peptide in vitro assays as in case of immunoepitope database have been more substantive than epitopes predicted by using in silico methods.
therefore, known and validated epitopes from the iedb
<cit>  are mapped on pvcs to predict potential immunogenic regions.
the jenner-predict server de-prioritizes pvc having human homolog as they can potentially cause autoimmunity
<cit>  or produce low immune response
<cit> .
conservation information of pvcs is provided by the web server to demonstrate their broad specificities.
since the web server provides conserved and potential immunogenic pvcs, it may be useful to replace the existing strain-specific vaccine candidates.
for example, the established vaccine candidate, pspa is having choline-binding protein  domain and it has limited application from vaccine point of view as it is strain-specific.
in contrast, our tool predicted cbpe  protein which is conserved across different strains of s. pneumoniae and has the same cbp domain.
since this protein is surface exposed, and involved in nasopharyngeal colonization and/or dissemination of s. pneumoniae which is important for virulence, this protein may further be explored for vaccine development process
<cit> .
jenner-predict server predicted  <dig> experimentally known promising pvcs, stk
<cit> , nana
<cit> , and psaa
<cit>  in s. pneumoniae which are containing domains from non-adhesins such as pasta, bnr and sbp, respectively  and they are known to be immunogenic.
similarly, our method predicted established non-adhesin vaccine candidates, omp a, irea, btub and tolc .
the wide-ranging applicability of the web server to all bacteria is substantiated by its high sensitivity for predicting diverse protective antigens from more than  <dig> pathogenic bacteria reported in protegen database  and dataset used for vaxijen server development .
our domain based method was effective in predicting many established non-adhesin vaccine candidates reported in protegen database
<cit>  such as  <dig> toxins,  <dig> binding proteins ,  <dig> membrane proteins,  <dig> surface proteins, etc.
which were not predicted by other methods.
these protective antigens are involved in many important pathogenesis processes like virulence, invasion, colonization, iron acquisition, osmo-regulation, etc.
.
the sensitivity of the web server was further substantiated by its prediction of immunogenic protein, q <dig>  from negative dataset used for training of vaxijen server ).
higher sensitivity and specificity of jenner-predict server  justifies the domains involved in host-pathogen interactions and pathogenesis are better criteria for pvcs prediction than other existing approaches.
CONCLUSIONS
the jenner-predict server has been developed to predict potential pvcs and to provide their vaccine potential with an objective of assisting subunit vaccine development.
the web server was validated on independent and diverse datasets, where it outperformed other pvc prediction tools.
its performance substantiated that the proteins involved in host-pathogen interactions and pathogenesis are better criteria than methods based on machine learning or adhesin-likeliness.
our method predicts less number of proteins with high prediction accuracy which confirms its reliability.
mapping of known epitopes from iedb database on pvcs increases the probability of a protein to be immunogenic.
comparison of these pvcs with human proteome sequences reduces the chance of their failure due to autoimmunity.
conservation of pvcs in pathogenic strains provides crucial information on their broad-specificities.
the web server demonstrated that domain-based method can be used to predict pvcs from pathogen proteomes.
since the web server provides prioritized pvcs, few prospective proteins from a proteome could be taken for experimental evaluation to identify subunit vaccine candidates.

 in silico prediction has been proved to be of great significance among various disciplines of life sciences including biomedical research. the conventional vaccine development methods are time consuming as they require cultivation of pathogenic microorganisms in laboratory conditions and their dissection using microbiological, biochemical and immunological methods in order to identify the components important for immunogenecity. these methods are ineffective in circumstances where the cultivation of bacteria is difficult or impossible. the other limitations arise when the expression of protective antigens is less or absent in in vitro conditions compared to in vivo diseased conditions. in comparison to conventional live attenuated vaccines, subunit vaccines are more reliable as far as safety is concerned. vaccine candidate identification is an essential and important component in subunit vaccine development. the integration of genomics in vaccine research is expected to revolutionize novel vaccine candidate identification. computational approach, especially reverse vaccinology method assists the identification of vaccine candidates from genomes without culturing microorganisms and thus facilitates the subunit vaccine development. these methods are useful in reducing time, cost and number of wet lab experiments. the rv is a computational pipeline for identification of vaccine candidates against microorganisms from their genome sequences. thus, all proteins of an organism can be screened computationally for their vaccine potential. significant success of this principle for vaccine development had already been demonstrated in several pathogens, including neisseria meningitides, helicobacter pylori, streptococcus pneumoniae, porphyromonas gingivalis, chlamydia pneumoniae and bacillus anthracis. the relevance of this method was recognized when vaccines developed from capsular polysaccharides of n. meningitides b failed due to cross reactivity against human tissue. application of rv techniques for pvc identification and then in vivo testing led to the development of licensed broad specificity protein vaccine, 5cvmb, against n. meningitides. this vaccine contains protein antigen components, gna gna gna gna and nada, which were primarily discovered by rv methods. however in earlier rv techniques, protein localization was used as the main criterion for identification of pvcs. as a result, a large number of proteins were required to be expressed, purified and tested to obtain few vaccine candidates leading to enormous loss of cost and time. on the other hand, identifying immunogenic proteins by using epitope prediction software and web servers have several limitations. comparative studies have shown that b-cell epitopes and class-ii mhc-binding t-cell epitopes prediction methods are not accurate. over-prediction, inability in exact position prediction of epitopes and absence of success in identifying known epitopes in proteins are major concerns in vaccine candidate identification. until now the available pvcs prediction software and web servers have not been much effective for identification of vaccine candidates from genomes for vaccine design. vaxijen server, based on discriminant analysis and partial least square methods, was developed by using datasets of known protective antigenic and non-antigenic proteins to predict pvcs. surprisingly, it predicts more than half of proteins from a given bacterial proteome as protective antigens with default parameters making its usage almost impractical. further, existing software and web servers predict different proteins as vaccine candidates from same proteome sequences. for example, different proteins were predicted from s. pneumoniae proteome by vaxijen server and new enhanced reverse vaccinology environment software. from proteins of s. pneumoniae, vaxijen and nerve predicted and as pvcs, respectively while only proteins were common between them. none of the common pvcs matched with known vaccine candidates in s. pneumoniae. this outcome complicates decision process regarding which tool’s output should be taken for experimental testing to identify vaccine candidates. the method used in nerve and vaxign tools presumed that extracellular proteins having adhesin-likeliness are potential vaccine candidates. although adhesin-likeliness of a protein is an important criterion, it should not be considered as the only one because several non-adhesin functional classes of proteins are also involved in host-pathogen interactions or pathogenesis and many of them are known to be antigenic. it has been suggested that targeting host-pathogen interactions and disease processes at molecular level can be used for novel vaccine discovery. in several cases, the immune responses against these non-adhesins were known to provide protection against microbial infection. invasin, porin, flagellin and toxin have roles in host cell invasion; transportation activity is associated with pathogenesis and virulence; chemotaxis, adhesion and colonization are making pathogenic bacteria to be virulent; and host cell death, respectively. bacterial fibronectin-binding proteins target host fibronectin for adhesion and colonization; transferrin-binding proteins are used by bacteria to obtain iron directly from host transferrins; and penicillin-binding proteins are involved in peptidoglycan biosynthesis to maintain cell wall structure and protection. the solute binding proteins are used to capture nutrients like iron to overcome the environment devoid of free nutrients within the host. choline-binding proteins in some bacteria perform adhesin-like function. functional classes of proteins involved in virulence, invasion and colonization; porins and flagellin; and binding proteins of choline, penicillin, transferrin, fibronectin and solute are important in host-pathogen interactions and pathogenesis. since many proteins from these functional classes provide protective immune responses against microbial infection, the knowledge of host-pathogen interactions related to bacterial pathogenesis could be used to rationalize and improve vaccine candidate prediction. a web server, jenner-predict, has been developed which is capable of predicting pvcs from proteome/protein sequences. it is based on the principle that non-cytosolic proteins having functions important in host-pathogen interactions and/or pathogenesis are potential vaccine candidates. it has two broad components: pvcs prediction and analysis of their vaccine potential. the pvcs prediction is performed in three sequential steps: prediction of subcellular localization, expressibility in laboratory and presence of domains critical in host-pathogen interactions and pathogenesis. software psortb is used for protein subcellular localization prediction. a protein has high probability of failure to express in experiment when it has more trans-membrane helices. hmmtop software is used for topology prediction and proteins with more than two trans-membrane helices are discarded. proteins pass through above two filters, and having domains involved in host-pathogen interactions and pathogenesis from functional classes of adhesin, invasin, toxin, porins, colonization, virulence, flagellin, penicillin-binding, choline-binding transferring-binding, fibronectin-binding and solute-binding proteins are selected as vaccine candidates. standalone pfam sequence search is used for prediction of domains. vaccine potential of pvcs is predicted on the basis of their possible immunogenicity, absence of autoimmunity, and conservation across different pathogenic and non-pathogenic strains of same bacteria. known bces and tces from immune epitope database are mapped separately on predicted pvcs to know their possible immunogenic region and potential. this mapping of antigenic determinant is instrumental in predicting humoral or cellular or both immune responses of pvc. since pvcs specific to pathogenic strains are expected to be involved in virulence, therefore conservation of pvcs in different pathogenic strains of same organism is determined to provide more robust vaccine candidates. the pvcs having homolog in host are provided by the web server. such pvcs may produce autoimmunity or less immune response. taking into account above criteria, output of the web server is provided as prioritized pvcs in the result table. comparison among pvc prediction methods has shown that jenner-predict server’s performance is better.  the web server, jenner-predict, has been developed to predict pvcs from proteome or protein sequences for subunit vaccine development on the basis of domains critical to host-pathogen interactions and pathogenesis. besides predicting pvcs, it also furnishes information crucial to determine their vaccine capability in terms of immunogenic potential by matching pvcs against iedb epitopes, autoimmunity through matching pvcs with human proteome, and their conservation across different pathogenic and non-pathogenic strains of the organism. a tutorial explaining how to submit a job as well as user-friendly interpretation of results is available at the web server’s home page. the web server gives higher priority to pvcs containing more iedb epitope matches as they increase their possibility to be immunogenic. the pvcs containing exact iedb epitopes match are shown in white background. the web server also decreases priority of pvcs having human homologs as such pvcs should be discouraged from further vaccine development process. this prioritization is instrumental in selecting few pvcs for further vaccine development experiments. the performance of the web server was evaluated against reported vaccine candidates in s. pneumoniae and e. coli, proteins {both positive and negative } used for the development of vaxijen server and protective antigens from more than bacteria reported in protegen database. pvcs prediction in s. pneumoniae and e. coli in s. pneumoniae proteome, jenner-predict server predicted proteins as vaccine candidates. as vaxijen server predicts more than half of a proteome as vaccine candidates in any bacteria with default vaxijen probability score,, a cut-off of was considered to restrict the number of pvcs so that the performance of this approach can be compared against all other methods. from s. pneumoniae proteome, our web server predicted out of known non-cytoplasmic pvcs whereas the software, nerve, and servers, vaxign and vaxijen, predicted only and pvcs, respectively. as compared to other methods, the pvcs predicted exclusively by jenner-predict server were stk, nana, and psaa that are having pasta, bnr, and sbp domains, respectively. other than reported vaccine candidates from pvcs, our web server predicted abc-transporters and solute-binding, choline-binding, penicillin-binding, lysm-containing domain, cell wall anchors, etc. abc-transporter, choline-binding and penicillin-binding proteins are known to be potential pvcs in different bacteria including s. pneumoniae. in e. coli proteome, jenner-predict server predicted proteins as vaccine candidates whereas the nerve and vaxijen predicted more than and proteins, respectively. our web server predicted out of known pvcs whereas software, nerve and servers, vaxign and vaxijen, predicted and pvcs, respectively. the pvcs missed out by other methods due to being non-adhesins were ompa, btub, tolc and irea. besides known protective antigens, the majority of predicted pvcs by our web server were bpd transporter proteins, solute-binding proteins and fimbrial proteins. prediction of pvcs against protegen database and datasets used in vaxijen server development the results of our web server for prediction of known protective vaccine candidates from more than diverse bacteria reported in protegen database and its comparison against other similar methods has been presented in additional file 6: table s our web server predicted out of protective antigens from protegen database whereas software, nerve, and servers, vaxign and vaxijen, predicted and respectively. the protective antigens which were only predicted by our method and skipped by others belong to functional classes of solute binding, toxin, invasin, etc.. the jenner-predict server was found to be efficient in discriminating between antigens and non-antigens. from the pas used in vaxijen server development, nerve, vaxign and vaxijen predicted and proteins, respectively whereas our web server predicted pvcs ). from negative dataset of proteins, the nerve, vaxign and vaxijen methods predicted and proteins to be vaccine candidates, respectively compared to proteins by our web server ). negative dataset proteins were considered as non-antigens. but one out of two pvcs predicted by our web server has already been known to be antigenic: fibronectin-attachment protein provides protective immunity against mycobacterium avium infection. validation of jenner-predict sensitivity and specificity indices of different pvc prediction methods have been presented in table  jenner-predict server’s pvcs prediction accuracy is better at all levels: bacterial proteomes, protegen database and datasets used for vaxijen server development. unavailability of total number of known vaccine candidates in a proteome prevented us to calculate sensitivity and specificity values for proteome sequences. the results of pvc prediction from two proteomes by different methods have been provided in table  detailed comparison of results w.r.t known vaccine candidates of s. pneumoniae and e. coli by different methods has been provided in additional file 1: table s and additional file 5: s respectively. on dataset used in development of vaxijen server, sensitivity and specificity of our tool were and, respectively whereas comparable methods nerve, vaxign and vaxijen have corresponding values and; and and and, respectively. for the protegen database, only sensitivity was calculated as specificity calculation was not feasible due to lack of negative dataset. the sensitivities of nerve, vaxign and vaxijen were, and, respectively as compared to for jenner-predict server. * see method section for details. # values within square bracket indicates total number of proteins vaccine candidates predicted by respective software/server. value within parenthesis indicates experimentally known protective vaccine candidates in that organism whereas the values in bold give the number of pvcs predicted by respective software/server from experimentally known protective vaccine candidates in that organism. $ values indicate the number of pvcs predicted by respective software/server from bacterial protective antigens taken for evaluation. % values indicate the number of pvcs predicted by respective software/server from and proteins for positive and negative datasets, respectively. discussion the motivation behind developing ‘jenner-predict’ web server is to provide credible vaccine candidates and information regarding their vaccine potential in terms of possible immunogenicity, absence of autoimmunity and conservation so that subunit vaccine development can be accelerated. the outcome of the web server has substantiated that domains involved in host-pathogen interactions are better criterion for prediction of pvcs than approaches dependent upon only adhesin-likeliness or machine learning. as pvcs are predicted based on their functions, biologists can assess the importance of a given function in pathogenesis for that organism. the information regarding the function of pvc could be instrumental for vaccine development. for example, colonization is crucial in streptococcus pathogenesis and proteins involved in this process were used as vaccine candidates. most of the earlier rv methods focused on outer membrane or secretory proteins of a proteome to identify pvcs. pizza et al. screened proteome sequences of n. meningitis to identify proteins which were probably surface exposed or involved in transportation and obtained proteins. out of them, proteins were expressed and experimentally tested for their immunogenic potential. finally, proteins were found to provide protective immunity against n. meningitides. similarly, wizemann et al. searched for motifs related to secretory or surface binding proteins in s. pneumoniae proteome and identified proteins containing such motifs. out of them, were expressed and tested for their protective immunity. finally, proteins were found as protective antigens. similar studies were performed for p. gingivalis and c. pneumoniae. although proteins providing immunity were identified in these studies but the number of experiments, cost and time requirement were enormous even for identifying pvcs from a particular localization. on the contrary, jenner-predict server is relying on protein domains involved in host-pathogen interactions for providing reasonably less number of prioritized vaccine candidates from a proteome. for better validation of vaccine candidates, the user may select few prospective vaccine candidates for experimental testing to verify their protective immunity. the bce and tce mapping algorithms were developed to identify possible immunogenic region and consequently prediction of immunogenic potential of a protein. but these methods have drawbacks of over-prediction and even predict epitope in known non-antigenic proteins. currently available antigen or pvc prediction methods were not validated on complete or diverse data. nerve software was evaluated on its prediction ability of popular vaccine candidates from five bacterial proteomes instead of all known vaccine candidates in those organisms. similarly, the vaxign server was evaluated against only limited number of known outer membrane protein vaccine candidates from uropathogenic e. coli. further, the vaxijen server was developed on limited data. even some of the sequences used as the negative data for web server development were predicted as vaccine candidates by all other methods ). our web server predicted two such proteins as pvcs from the negative dataset sequences. experimental data confirmed that alanine and proline rich secreted protein is immunogenic whereas the other protein is a periplasmic serine or membrane protease and has already been reported as protective antigen in haemophilus influenzae. the other pvcs predicted from negative dataset by nerve, vaxign and vaxijen have no evidence of being immunogenic. this outcome justifies higher sensitivity of our method. to provide prospective pvcs of a proteome, the predicted vaccine candidates are prioritized by jenner-predict server. the pvcs having more iedb epitope matches are ranked higher as such epitope match increases their possibility to be immunogenic. epitopes identified using ‘hands on’ peptide-by-peptide in vitro assays as in case of immunoepitope database have been more substantive than epitopes predicted by using in silico methods. therefore, known and validated epitopes from the iedb are mapped on pvcs to predict potential immunogenic regions. the jenner-predict server de-prioritizes pvc having human homolog as they can potentially cause autoimmunity or produce low immune response. conservation information of pvcs is provided by the web server to demonstrate their broad specificities. since the web server provides conserved and potential immunogenic pvcs, it may be useful to replace the existing strain-specific vaccine candidates. for example, the established vaccine candidate, pspa is having choline-binding protein domain and it has limited application from vaccine point of view as it is strain-specific. in contrast, our tool predicted cbpe protein which is conserved across different strains of s. pneumoniae and has the same cbp domain. since this protein is surface exposed, and involved in nasopharyngeal colonization and/or dissemination of s. pneumoniae which is important for virulence, this protein may further be explored for vaccine development process. jenner-predict server predicted experimentally known promising pvcs, stk, nana, and psaa in s. pneumoniae which are containing domains from non-adhesins such as pasta, bnr and sbp, respectively and they are known to be immunogenic. similarly, our method predicted established non-adhesin vaccine candidates, omp a, irea, btub and tolc. the wide-ranging applicability of the web server to all bacteria is substantiated by its high sensitivity for predicting diverse protective antigens from more than pathogenic bacteria reported in protegen database and dataset used for vaxijen server development. our domain based method was effective in predicting many established non-adhesin vaccine candidates reported in protegen database such as toxins, binding proteins, membrane proteins, surface proteins, etc. which were not predicted by other methods. these protective antigens are involved in many important pathogenesis processes like virulence, invasion, colonization, iron acquisition, osmo-regulation, etc.. the sensitivity of the web server was further substantiated by its prediction of immunogenic protein, q from negative dataset used for training of vaxijen server ). higher sensitivity and specificity of jenner-predict server justifies the domains involved in host-pathogen interactions and pathogenesis are better criteria for pvcs prediction than other existing approaches.  the jenner-predict server has been developed to predict potential pvcs and to provide their vaccine potential with an objective of assisting subunit vaccine development. the web server was validated on independent and diverse datasets, where it outperformed other pvc prediction tools. its performance substantiated that the proteins involved in host-pathogen interactions and pathogenesis are better criteria than methods based on machine learning or adhesin-likeliness. our method predicts less number of proteins with high prediction accuracy which confirms its reliability. mapping of known epitopes from iedb database on pvcs increases the probability of a protein to be immunogenic. comparison of these pvcs with human proteome sequences reduces the chance of their failure due to autoimmunity. conservation of pvcs in pathogenic strains provides crucial information on their broad-specificities. the web server demonstrated that domain-based method can be used to predict pvcs from pathogen proteomes. since the web server provides prioritized pvcs, few prospective proteins from a proteome could be taken for experimental evaluation to identify subunit vaccine candidates. 
the performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in protegen database and datasets used for vaxijen server development. it also provides vaccine potential of pvcs in terms of their possible immunogenicity by comparing with experimentally known iedb epitopes, absence of autoimmunity and conservation in different strains. therefore, knowledge of bacterial pathogenesis has potential to identify pvcs. subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. the jenner-predict server outperformed nerve, vaxign and vaxijen methods. several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. the web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. better prediction accuracy of jenner-predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of pvcs. protein vaccine candidates host-pathogen interactionsdomainantigenreverse vaccinologyvirulence the existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of pvcs. a web server, jenner-predict, has been developed for prediction of pvcs from proteomes of bacterial pathogens. the web server efficiently predicted known vaccine candidates reported from streptococcus pneumoniae and escherichia coli proteomes. the web server has successfully predicted maximum known pvcs belonging to different functional classes. jenner-predict server is freely accessible at http://. computational approach, especially reverse vaccinology method has enormous potential for identification of protein vaccine candidates from a proteome. predicted pvcs are prioritized so that only few prospective pvcs could be validated experimentally.
 subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. computational approach, especially reverse vaccinology method has enormous potential for identification of protein vaccine candidates from a proteome. the existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of pvcs. several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. therefore, knowledge of bacterial pathogenesis has potential to identify pvcs.  a web server, jenner-predict, has been developed for prediction of pvcs from proteomes of bacterial pathogens. the web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. it predicts non-cytosolic proteins containing above domains as pvcs. it also provides vaccine potential of pvcs in terms of their possible immunogenicity by comparing with experimentally known iedb epitopes, absence of autoimmunity and conservation in different strains. predicted pvcs are prioritized so that only few prospective pvcs could be validated experimentally. the performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in protegen database and datasets used for vaxijen server development. the web server efficiently predicted known vaccine candidates reported from streptococcus pneumoniae and escherichia coli proteomes. the jenner-predict server outperformed nerve, vaxign and vaxijen methods. it has sensitivity of and for protegen and vaxijen dataset, respectively while specificity of has been obtained for the latter dataset.  better prediction accuracy of jenner-predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of pvcs. the web server has successfully predicted maximum known pvcs belonging to different functional classes. jenner-predict server is freely accessible at http://. /vaccine/home.html protein vaccine candidates host-pathogen interactionsdomainantigenreverse vaccinologyvirulence 