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 peptide-repertoire 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.

Fig. 1.

Mapping the peptide binding landscape with docking and sequence design. Many independent flexible-backbone peptide docking simulations generate diverse samples in structure space. Simultaneous peptide sequence refinement focuses exploration to the higher-affinity subspace.

Fig. 2.

PFMs derived from combinatorial library assays and structural simulations agree in many key positions. (Left) Representative side-by-side comparisons of experimental (Left) and computational (Second Column) PFMs, as well as examples for specificity-determining interactions suggested by structural modeling (Third Column; peptide backbone is shown in green, interacting peptide and MHC side chains are depicted in yellow and pink, respectively), for two MHC proteins: B*08:01 (Upper) and B*51:01 (Lower). (Right) Quality of the structure-based PFMs using B*35:01 structure as a template. Per-position divergence from the experimental PFM was transformed to a P value and plotted using a color scale ranging from P = 0.5 to P = 1/(9 × 15) (by chance, one would expect roughly half the squares in the table to be completely white and one to be black); P values for the total divergence across the peptide (“All”) and restricted to primary and secondary anchor positions (“Anchors”) as defined by ref.  are shown to the right (primary and secondary anchor positions are indicated by “A” and “a,” respectively).

Fig. 3.

Binding prediction accuracy. Simulation-derived PBEMs for seven HLA-A (Upper) and twelve HLA-B proteins (Lower) were used to predict binding scores for corresponding peptides with experimentally determined binding affinity (6). Area under the ROC curve for discriminating binders from nonbinders is shown for self, as well as homology modeling, based PBEMs. Significantly discriminative binding predictions (MWW P < 0.01) are denoted by *; (un), self-structure unavailable; (self) template is a self-structure.

Fig. 4.

Binding preference divergence. (Left) Differential PFMs, showing, for each position, the amino acids predicted to bind one MHC protein more favorably than another. The height of each column is proportional to the specificity divergence at that position; the difference in amino acid frequencies determine the height and order of the corresponding letters. Structural models (second and third columns) suggest plausible connections between MHC sequence mismatches and peptide binding preference (see text for details; color scheme as in Fig. 2). (Right) Per-position specificity divergences for six pairs of MHC proteins, differing at one or two positions (mismatched amino acids are shown on the right). Positions at which particular MHC polymorphism has been experimentally shown to alter the binding preference are red boxed.

Fig. 5.

Binding peptide diversity. For each MHC protein, the average pairwise Hamming distance for the 1% of lowest energy binders is shown, indicating the diversity of its binder repertoire (template, A*02:01). Proteins are ordered by diversity, and average values for HLA-A and HLA-B proteins are shown as black bars. Specific subsets of proteins are denoted as red-framed or blue-colored bars; see text for details. An asterisk stands for a two-tailed t-test P value < 10^-3; ** indicates P values < 10^-10.

Fig. 6.

Structural simulations reveal covariation between peptide sequence positions. (A and B) A shared pocket formed by side chains of the MHC α₂-helix favors compensatory (large-small/small-large) changes at P₃ and P₅. (C) A network of hydrogen bonds contributes to positive correlation between R3 and E6. (D) Sequence preferences at P₂ are altered due to a shift in the peptide backbone (cyan vs. green) induced by proline at P₃, which disrupts a conserved peptide backbone hydrogen bond to Y99.