Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Abstract

Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptiderepertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations. C lass I MHC proteins selectively bind short (typically, 8-10 amino acids) peptides derived from proteasomal degradation of cytosolic proteins and present these peptides on the cell surface for surveillance by CD8+ T lymphocytes. By this mechanism, non-self-peptides derived from intracellular pathogens can be detected by the immune system, and infected cells can be targeted for destruction. The MHC proteins are highly polymorphic (the class I MHC gene HLA-B is estimated to be the most polymorphic gene in the human genome; ref. 1), with different proteins recognizing specific and often quite divergent peptide repertoires. MHC polymorphism has been reported to play a role in a wide range of phenomena including susceptibility to infectious and inflammatory diseases, drug toxicity, autoimmunity, cancer, and transplantation outcome (2-5). The specific role of peptide-repertoire variation in each context is not well understood, in part because comprehensive characterization of the peptide binding preferences of individual MHC proteins is experimentally challenging. As a result, there has been great interest in computational models of MHC-peptide recognition. This interest has led to the development of machine-learning algorithms that are able to predict-in many cases, with high accuracy-the binding of a novel peptide to a target MHC molecule by training on experimental binding data for that protein or related MHC family members (6-10).The X-ray crystallographic structure determination of the first MHC protein (11) and, later, of several hundred peptide-MHC complexes, has shed light on the physicochemical attributes facilitating the formation o...

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