Scrutinizing human MHC polymorphism: Supertype analysis using Poisson-Boltzmann electrostatics and clustering

The pockets guide to HLA class I molecules

Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells. These peptides are bound within the peptide binding cleft of HLA, and together as a complex, are recognised by T cells using their specialised T cell receptors. Within the cleft, the peptide residue side chains bind into distinct pockets. These pockets ultimately determine the specificity of peptide binding. As HLAs are the most polymorphic molecules in humans, amino acid variants in each binding pocket influences the peptide repertoire that can be presented on the cell surface. Here, we review each of the 6 HLA binding pockets of HLA class I (HLA-I) molecules. The binding specificity of pockets B and F are strong determinants of peptide binding and have been used to classify HLA into supertypes, a useful tool to predict peptide binding to a given HLA. Over the years, peptide binding prediction has also become more reliable by using binding affinity and mass spectrometry data. Crystal structures of peptide-bound HLA molecules provide a means to interrogate the interactions between binding pockets and peptide residue side chains. We find that most of the bound peptides from these structures conform to binding motifs determined from prediction software and examine outliers to learn how these HLAs are stabilised from a structural perspective.

Sequence-structure-function relationships in class I MHC: A local frustration perspective

Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, human MHC (Human Leukocyte Antigen, HLA) is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with HLA in a common diagonal binding mode, and KIR-HLA interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the human MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.

APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations

The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.