ArticleBMC biotechnology2006
NERVE: new enhanced reverse vaccinology environment.
Article in BMC biotechnology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
56 citing papers in PubMed.
- PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision.Bioinformatics (Oxford, England) · 2026Article
- AntiPan: a genome-informed in silico pipeline for advancing subunit vaccine discovery against Staphylococcus aureus.Scientific reports · 2026Article
- Artificial intelligence in vaccine development: applications, implementation, and future directions.Frontiers in cellular and infection microbiology · 2026Review
- B-vac a robust software package for bacterial vaccine design.Scientific reports · 2025Article
- Article
- Challenges and opportunities in mRNA vaccine development against bacteria.Nature microbiology · 2025Review
- Molecular insights into pangenome localization and constructs design for Hemophilus influenza vaccine.Scientific reports · 2025Article
- Article
- A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens.PloS one · 2025Article
- 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
- Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy.BMC microbiology · 2024Review
- Review
- Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.bioRxiv : the preprint server for biology · 2023Article
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- Review
- Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics.Journal of fungi (Basel, Switzerland) · 2023Review
- Comparative Genomics ofJournal of fungi (Basel, Switzerland) · 2023Article
- Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of Bacterial Immunogenicity by Machine Learning Methods.Methods in molecular biology (Clifton, N.J.) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince a milestone work on Neisseria meningitidis B, Reverse Vaccinology has strongly enhanced the identification of vaccine candidates by replacing several experimental tasks using in silico prediction steps. These steps have allowed scientists to face the selection of antigens from the predicted proteome of pathogens, for which cell culture is difficult or impossible, saving time and money. However, this good example of bioinformatics-driven immunology can be further developed by improving in silico steps and implementing biologist-friendly tools.
resultsWe introduce NERVE (New Enhanced Reverse Vaccinology Environment), an user-friendly software environment for the in silico identification of the best vaccine candidates from whole proteomes of bacterial pathogens. The software integrates multiple robust and well-known algorithms for protein analysis and comparison. Vaccine candidates are ranked and presented in a html table showing relevant information and links to corresponding primary data. Information concerning all proteins of the analyzed proteome is not deleted along selection steps but rather flows into an SQL database for further mining and analyses.
conclusionAfter learning from recent years' works in this field and analysing a large dataset, NERVE has been implemented and tuned as the first available tool able to rank a restricted pool (approximately 8-9% of the whole proteome) of vaccine candidates and to show high recall (approximately 75-80%) of known protective antigens. These vaccine candidates are required to be "safe" (taking into account autoimmunity risk) and "easy" for further experimental, high-throughput screening (avoiding possibly not soluble antigens). NERVE is expected to help save time and money in vaccine design and is available as an additional file with this manuscript; updated versions will be available at http://www.bio.unipd.it/molbinfo.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.