ArticleJournal of computer-aided molecular design2001
Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach.
Article in Journal of computer-aided molecular design, 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 32 citations in OpenAlex.
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.Briefings in bioinformatics · 2020Review
- POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.Frontiers in immunology · 2020Article
- Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.Journal for immunotherapy of cancer · 2019Article
- Computational Prediction of the Epitopes of HA1 Protein of Influenza Viruses to its Neutralizing Antibodies.Antibodies (Basel, Switzerland) · 2018Article
- Computational Identification of Antibody Epitopes on the Dengue Virus NS1 Protein.Molecules (Basel, Switzerland) · 2017Article
- Computer-aided vaccine designing approach against fish pathogens Edwardsiella tarda and Flavobacterium columnare using bioinformatics softwares.Drug design, development and therapy · 2016Article
- Article
- Computational identification of epitopes in the glycoproteins of novel bunyavirus (SFTS virus) recognized by a human monoclonal antibody (MAb 4-5).Journal of computer-aided molecular design · 2013Article
- T-cell epitope vaccine design by immunoinformatics.Open biology · 2013Review
- Article
- Recovery of known T-cell epitopes by computational scanning of a viral genome.Journal of computer-aided molecular design · 2002Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].
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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.