ArticleProtein science : a publication of the Protein Society2000
Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles.
Article in Protein science : a publication of the Protein Society, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed.
- Analysis of HLA A*02 association with vaccine efficacy in the RV144 HIV-1 vaccine trial.Journal of virology · 2014Trial
- Immunoinformatics-driven multi-epitope vaccine design targeting PSMA, STEAP1, and B7H3 for prostate cancer.Frontiers in medicine · 2026Article
- Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review).International journal of oncology · 2025Review
- Immunogenicity ofPathogens (Basel, Switzerland) · 2025Article
- Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.Briefings in bioinformatics · 2021Article
- Advanced strategies for development of vaccines against human bacterial pathogens.World journal of microbiology & biotechnology · 2021Review
- Main Strategies for the Identification of Neoantigens.Cancers · 2020Review
- Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.Journal of immunology (Baltimore, Md. : 1950) · 2020Article
- Postgenomic Approaches and Bioinformatics Tools to Advance the Development of Vaccines against Bacteria of theVaccines · 2018Review
- Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.Current topics in medicinal chemistry · 2018Review
- PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity.GigaScience · 2017Article
- DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles.BMC structural biology · 2017Article
- modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.Biology direct · 2016Article
- Chemical Modification of Influenza CD8+ T-Cell Epitopes Enhances Their Immunogenicity Regardless of Immunodominance.PloS one · 2016Article
- Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNFα Biologics: Implications for Immunogenicity of Pharmaceutical Proteins.PloS one · 2015Article
- Thermodynamics of Peptide-MHC Class II Interactions: Not all Complexes are Created Equal.Frontiers in immunology · 2013Review
- A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.Computational biology and chemistry · 2013Article
- T-cell epitope vaccine design by immunoinformatics.Open biology · 2013Review
- EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.BMC bioinformatics · 2012Article
- Resistance-associated epitopes of HIV-1C-highly probable candidates for a multi-epitope vaccine.Immunogenetics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.
Indexed as
Identifiers
What OpenQuestion holds
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.