ArticleBMC immunology2008
Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research.
Article in BMC immunology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.
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Who cites it
117 citing papers in PubMed, 1 synthesis or guideline pooled it, 242 citations in OpenAlex.
- Varicella-zoster virus-derived major histocompatibility complex class I-restricted peptide affinity is a determining factor in the HLA risk profile for the development of postherpetic neuralgia.Journal of virology · 2015Pooled it
- HIV-1 vaccine-induced T-cell responses cluster in epitope hotspots that differ from those induced in natural infection with HIV-1.PLoS pathogens · 2013Trial
- In Silico Design and Evaluation of TcGAPDH as a Vaccine Against Chagas Disease: A Reverse Vaccinology Approach.Acta parasitologica · 2026Article
- DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction.BMC genomics · 2023Article
- Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies.Frontiers in oncology · 2023Review
- Repertoire-scale measures of antigen binding.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A comprehensive analysis of the IEDB MHC class-I automated benchmark.Briefings in bioinformatics · 2022Article
- Evaluation and Designing of Epitopic-Peptide Vaccine AgainstInternational journal of peptide research and therapeutics · 2022Article
- Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.Briefings in bioinformatics · 2021Article
- In-silico design of envelope based multi-epitope vaccine candidate against Kyasanur forest disease virus.Scientific reports · 2021Article
- Review
- Review
- SIMON: Open-Source Knowledge Discovery Platform.Patterns (New York, N.Y.) · 2021Article
- From Infection to Immunity: Understanding the Response to SARS-CoV2 ThroughFrontiers in immunology · 2021Article
- A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach.Frontiers in immunology · 2021Article
- The neglected challenge: Vaccination against rickettsiae.PLoS neglected tropical diseases · 2020Review
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.Briefings in bioinformatics · 2020Review
- Immunoinformatics: Predicting Peptide-MHC Binding.Annual review of biomedical data science · 2020Article
- Experimental Antiglomerular Basement Membrane GN Induced by a Peptide fromJournal of the American Society of Nephrology : JASN · 2020Article
- Epitope-Based Vaccine Designing ofInternational journal of peptide research and therapeutics · 2020Article
57 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundProtein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.
resultsOf 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).
conclusionNon-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction - a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.
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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.