Cargando…
Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions
BACKGROUND: 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 (RV) method has enormous potential for identification of protein...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701604/ https://www.ncbi.nlm.nih.gov/pubmed/23815072 http://dx.doi.org/10.1186/1471-2105-14-211 |
_version_ | 1782275674393804800 |
---|---|
author | Jaiswal, Varun Chanumolu, Sree Krishna Gupta, Ankit Chauhan, Rajinder S Rout, Chittaranjan |
author_facet | Jaiswal, Varun Chanumolu, Sree Krishna Gupta, Ankit Chauhan, Rajinder S Rout, Chittaranjan |
author_sort | Jaiswal, Varun |
collection | PubMed |
description | BACKGROUND: 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 (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) 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. RESULTS: 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 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset. CONCLUSIONS: 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://117.211.115.67/vaccine/home.html |
format | Online Article Text |
id | pubmed-3701604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37016042013-07-05 Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions Jaiswal, Varun Chanumolu, Sree Krishna Gupta, Ankit Chauhan, Rajinder S Rout, Chittaranjan BMC Bioinformatics Methodology Article BACKGROUND: 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 (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) 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. RESULTS: 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 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset. CONCLUSIONS: 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://117.211.115.67/vaccine/home.html BioMed Central 2013-07-01 /pmc/articles/PMC3701604/ /pubmed/23815072 http://dx.doi.org/10.1186/1471-2105-14-211 Text en Copyright © 2013 Jaiswal et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Jaiswal, Varun Chanumolu, Sree Krishna Gupta, Ankit Chauhan, Rajinder S Rout, Chittaranjan Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title | Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title_full | Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title_fullStr | Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title_full_unstemmed | Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title_short | Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions |
title_sort | jenner-predict server: prediction of protein vaccine candidates (pvcs) in bacteria based on host-pathogen interactions |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701604/ https://www.ncbi.nlm.nih.gov/pubmed/23815072 http://dx.doi.org/10.1186/1471-2105-14-211 |
work_keys_str_mv | AT jaiswalvarun jennerpredictserverpredictionofproteinvaccinecandidatespvcsinbacteriabasedonhostpathogeninteractions AT chanumolusreekrishna jennerpredictserverpredictionofproteinvaccinecandidatespvcsinbacteriabasedonhostpathogeninteractions AT guptaankit jennerpredictserverpredictionofproteinvaccinecandidatespvcsinbacteriabasedonhostpathogeninteractions AT chauhanrajinders jennerpredictserverpredictionofproteinvaccinecandidatespvcsinbacteriabasedonhostpathogeninteractions AT routchittaranjan jennerpredictserverpredictionofproteinvaccinecandidatespvcsinbacteriabasedonhostpathogeninteractions |