ArticleJournal of biomedicine & biotechnology2010
MHC Class II Binding Prediction-A Little Help from a Friend.
Article in Journal of biomedicine & biotechnology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- A peptide derived from HSP60 reduces proinflammatory cytokines and soluble mediators: a therapeutic approach to inflammation.Frontiers in immunology · 2023Review
- In Silico Prediction of Human Leukocytes Antigen (HLA) Class II Binding Hepatitis B Virus (HBV) Peptides in Botswana.Viruses · 2020Article
- MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model.Bioinformatics (Oxford, England) · 2020Article
- Epitope-Binding Characteristics for Risk versus Protective DRB1 Alleles for Visceral Leishmaniasis.Journal of immunology (Baltimore, Md. : 1950) · 2018Article
- Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions.Frontiers in immunology · 2018Article
- Computational Identification and Characterization of a Promiscuous T-Cell Epitope on the Extracellular Protein 85B ofBioMed research international · 2017Article
- Specificities of human CD4+ T cell responses to an inactivated flavivirus vaccine and infection: correlation with structure and epitope prediction.Journal of virology · 2014Article
- Article
- The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection.Journal of immunology (Baltimore, Md. : 1950) · 2012Article
- PeptX: using genetic algorithms to optimize peptides for MHC binding.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.
Identifiers
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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.