Abstract

Peptide binding to MHC receptors is part of a central biological process that enables our immune system to attack diseased cells. We use molecular simulations to illuminate the mechanisms driving stable peptide–MHC binding. Our simulation framework produces an atomistic model of the unbinding dynamics for a given peptide–MHC, which quantifies transitions between the major states of the system (bound, intermediate, and unbound). We applied this framework to study the binding of a SARS-CoV peptide to the HLA-A*24:02 receptor. This work revealed the unexpected importance of peptide’s position 4 in driving the stability of the complex, a finding with broader biomedical implications. Our methods can be applied to other peptide–MHC complexes, requiring only a 3D model as input.Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.Code for umbrella sampling, adaptive sampling, and MSM analysis, as well as representative structures, can be found in Github at https://github.com/KavrakiLab/adaptive-samplingpmhc. Simulation data are available upon request.

Publisher: http://dx.doi.org/10.1073/pnas.2007246117

PDF preprint: http://kavrakilab.org/publications/abella2020-pnas.pdf