title: Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment.
abstract: The presentation of potentially pathogenic peptides by major histocompatibility complex (MHC) molecules is one of the most important processes in adaptive immune defense. Prediction of peptide-MHC (pMHC) binding affinities is therefore a principal objective of theoretical immunology. Machine learning techniques achieve good results if substantial experimental training data are available. Approaches based on structural information become necessary if sufficiently similar training data are unavailable for a specific MHC allele, although they have often been deemed to lack accuracy. In this study, we use a free energy method to rank the binding affinities of 12 diverse peptides bound by a class I MHC molecule HLA-A*02:01. The method is based on enhanced sampling of molecular dynamics calculations in combination with a continuum solvent approximation and includes estimates of the configurational entropy based on either a one or a three trajectory protocol. It produces precise and reproducible free energy estimates which correlate well with experimental measurements. If the results are combined with an amino acid hydrophobicity scale, then an extremely good ranking of peptide binding affinities emerges. Our approach is rapid, robust, and applicable to a wide range of ligand-receptor interactions without further adjustment.
authors.affiliation: Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, United Kingdom.
authors.forename: Shunzhou
authors.initials: S
authors.lastname: Wan
authors.affiliation: Protein Informatics Group, Department of Statistics, University of Oxford , Oxford, OX1 3TG, United Kingdom.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Knapp
authors.affiliation: Institute of Structural and Molecular Biology, University College London , London WC1E 6BT, United Kingdom.
authors.forename: David W
authors.initials: DW
authors.lastname: Wright
authors.affiliation: Protein Informatics Group, Department of Statistics, University of Oxford , Oxford, OX1 3TG, United Kingdom.
authors.forename: Charlotte M
authors.initials: CM
authors.lastname: Deane
authors.affiliation: Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, United Kingdom.
authors.forename: Peter V
authors.initials: PV
authors.lastname: Coveney
mesh_terms: D015789:HLA-A2 Antigen / Q000737:chemistry*; D008285:Major Histocompatibility Complex; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; D010455:Peptides