Peptide exchange on MHC-I by TAPBPR is driven by a negative allostery release cycle.

Chaperones TAPBPR and tapasin associate with class I major histocompatibility complexes (MHC-I) to promote optimization (editing) of peptide cargo. Here, we use solution NMR to investigate the mechanism of peptide exchange. We identify TAPBPR-induced conformational changes on conserved MHC-I molecular surfaces, consistent with our independently determined X-ray structure of the complex. Dynamics present in the empty MHC-I are stabilized by TAPBPR and become progressively dampened with increasing peptide occupancy. Incoming peptides are recognized according to the global stability of the final pMHC-I product and anneal in a native-like conformation to be edited by TAPBPR. Our results demonstrate an inverse relationship between MHC-I peptide occupancy and TAPBPR binding affinity, wherein the lifetime and structural features of transiently bound peptides control the regulation of a conformational switch located near the TAPBPR binding site, which triggers TAPBPR release. These results suggest a similar mechanism for the function of tapasin in the peptide-loading complex

Interaction Pattern of Arg 62 in the A-Pocket of Differentially Disease-Associated HLA-B27 Subtypes Suggests Distinct TCR Binding Modes

The single amino acid replacement Asp116His distinguishes the two subtypes HLA-B*2705 and HLA-B*2709 which are, respectively, associated and non-associated with Ankylosing Spondylitis, an autoimmune chronic inflammatory disease. The reason for this differential association is so far poorly understood and might be related to subtype-specific HLA:peptide conformations as well as to subtype/peptide-dependent dynamical properties on the nanoscale. Here, we combine functional experiments with extensive molecular dynamics simulations to investigate the molecular dynamics and function of the conserved Arg62 of the α1-helix for both B27 subtypes in complex with the self-peptides pVIPR (RRKWRRWHL) and TIS (RRLPIFSRL), and the viral peptides pLMP2 (RRRWRRLTV) and NPflu (SRYWAIRTR). Simulations of HLA:peptide systems suggest that peptide-stabilizing interactions of the Arg62 residue observed in crystal structures are metastable for both B27 subtypes under physiological conditions, rendering this arginine solvent-exposed and, probably, a key residue for TCR interaction more than peptide-binding. This view is supported by functional experiments with conservative (R62K) and non-conservative (R62A) B*2705 and B*2709 mutants that showed an overall reduction in their capability to present peptides to CD8+ T cells. Moreover, major subtype-dependent differences in the peptide recognition suggest distinct TCR binding modes for the B*2705 versus the B*2709 subtype

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

<p>Abstract</p> <p>Background</p> <p>The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different <it>in silico </it>techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain <it>in silico </it>scoring functions?</p> <p>Results</p> <p>Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.</p> <p>Conclusion</p> <p>We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.</p