Exploring peptide/MHC detachment processes using Hierarchical Natural Move Monte Carlo

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Bioinformatics, btv502 (2015) .


Abstract

Motivation: The binding between a peptide and an 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 binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC. Results: In 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 (AROC 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 peptide/MHC, the peptide does not fully detach. Our approach is orders of magnitude faster and as such allows us to explore peptide/MHC detachment processes in a way not possible with all-atom Molecular Dynamics simulations. Availability: The source code is freely available for download at http://www.cs.ox.ac.uk/mosaics/



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