ArticleJournal of immunology (Baltimore, Md. : 1950)2020
Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.
Article in Journal of immunology (Baltimore, Md. : 1950), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
- Improved prediction of MHC-peptide binding using protein language models.Frontiers in bioinformatics · 2023Article
- 3pHLA-score improves structure-based peptide-HLA binding affinity prediction.Scientific reports · 2022Article
- Promoting the formation of Pi-stacking interaction to improve CTL cells activation between modified peptide and HLA.American journal of translational research · 2022Article
- Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders.Frontiers in molecular biosciences · 2021Article
- T Cell Epitope Prediction and Its Application to Immunotherapy.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell-based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D
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