ArticleBioinformatics (Oxford, England)2016
Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo.
Article in Bioinformatics (Oxford, England), 2016. 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.
- Article
- Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Hierarchical natural move Monte Carlo refines flexible RNA structures into cryo-EM densities.RNA (New York, N.Y.) · 2020Article
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.Briefings in bioinformatics · 2020Review
- MHC binding affects the dynamics of different T-cell receptors in different ways.PLoS computational biology · 2019Article
- HLA-DM Stabilizes the Empty MHCII Binding Groove: A Model Using Customized Natural Move Monte Carlo.Journal of chemical information and modeling · 2019Article
- How peptide/MHC presence affects the dynamics of the LC13 T-cell receptor.Scientific reports · 2019Article
- The partial dissociation of MHC class I-bound peptides exposes their N terminus to trimming by endoplasmic reticulum aminopeptidase 1.The Journal of biological chemistry · 2018Article
- General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept.Scientific reports · 2018Article
- Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.Current topics in medicinal chemistry · 2018Review
- Modeling Functional Motions of Biological Systems by Customized Natural Moves.Biophysical journal · 2016Article
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4 authors.
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Abstract
motivationThe binding between a peptide and a major histocompatibility complex (MHC) is one of the most important processes for the induction of an adaptive immune response. Many algorithms have been developed to predict peptide/MHC (pMHC) binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC.
resultsIn this study, we used a combination of coarse graining, hierarchical natural move Monte Carlo and stochastic conformational optimization to explore the detachment processes of 32 different peptides from HLA-A*02:01. We performed 100 independent repeats of each stochastic simulation and found that the presence of experimentally known anchor amino acids affects the detachment trajectories of our peptides. Comparison with experimental binding affinity data indicates the reliability of our approach (area under the receiver operating characteristic curve 0.85). We also compared to a 1000 ns molecular dynamics simulation of a non-binding peptide (AAAKTPVIV) and HLA-A*02:01. Even in this simulation, the longest published for pMHC, the peptide does not fully detach. Our approach is orders of magnitude faster and as such allows us to explore pMHC detachment processes in a way not possible with all-atom molecular dynamics simulations. AVAILABILITY AND IMPLEMENTATION: The source code is freely available for download at http://www.cs.ox.ac.uk/mosaics/. CONTACT: bernhard.knapp@stats.ox.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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