ArticleBMC bioinformatics2013
Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 83 citations in OpenAlex.
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- NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface.BMC bioinformatics · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
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- Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics.Journal of fungi (Basel, Switzerland) · 2023Review
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
- Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets.Pathogens (Basel, Switzerland) · 2022Review
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- SILVI, an open-source pipeline for T-cell epitope selection.PloS one · 2022Article
- An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.Nucleic acids research · 2021Article
- Article
- Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.Bioinformatics (Oxford, England) · 2020Article
- Identification ofFrontiers in microbiology · 2020Article
- Immunoinformatics and Vaccine Development: An Overview.ImmunoTargets and therapy · 2020Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSubunit 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.
resultsA 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.
conclusionsBetter 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.
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