ArticleProceedings of the National Academy of Sciences of the United States of America2011
Large-scale characterization of peptide-MHC binding landscapes with structural simulations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Challenges targeting cancer neoantigens in 2021: a systematic literature review.Expert review of vaccines · 2021Pooled it
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
- Structural evolution of polymorphic class I HLA alleles, designating HLA-C with uncertain assemblies.Computational and structural biotechnology journal · 2026Article
- Identification and biophysical characterization of Plasmodium peptide binding by common African HLAs.Scientific reports · 2025Article
- A novel C2H2-type zinc-finger transcription factor, CitZAT4, regulates ethylene-induced orange coloration in Satsuma mandarin flavedo (Citrus unshiu Marc.).Journal of integrative plant biology · 2025Article
- RACER-m leverages structural features for sparse T cell specificity prediction.Science advances · 2024Article
- HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.Nature communications · 2023Article
- CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks.Communications biology · 2023Article
- Overexpression of MdZAT5, an C2H2-Type Zinc Finger Protein, Regulates Anthocyanin Accumulation and Salt Stress Response in Apple Calli andInternational journal of molecular sciences · 2022Article
- Structural Prediction of Peptide-MHC Binding Modes.Methods in molecular biology (Clifton, N.J.) · 2022Review
- 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
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.Briefings in bioinformatics · 2020Review
- Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens.Frontiers in medical technology · 2020Article
- Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests.Frontiers in immunology · 2020Article
- Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors.Frontiers in genetics · 2019Review
- Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.PLoS computational biology · 2018Article
- Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring.Molecular immunology · 2018Article
- Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.Current topics in medicinal chemistry · 2018Review
- A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations.Frontiers in immunology · 2018Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptide-repertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations.
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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.