Structural prediction of peptides binding to MHC class I molecules

Abstract

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

“…Peptide threading and docking methods do not perform exhaustive figurations of the MHC-peptide structure. Nevertheless, accurate prediction of MHC-peptide structures can certainly improve the accuracy of peptide binding predictions and relevant efforts have been made to model the appropriate geometry of MHCI-and MHCIIbound peptides [92][93][94][95][96].…”

Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens

“…[20][21][22][23][24][25][26][27][28][29]. These methods are also known as "ab initio" methods because they do not require the experimental HLA binding data.…”

Structure-based prediction of MHC–peptide association: Algorithm comparison and application to cancer vaccine design

“…We have recently published an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces) that allows flexible peptide structure prediction in the class I MHC binding groove with incorporation of explicit water molecules at the peptide-MHC interface [30]; interface solvation is achieved through a second algorithm, WATGEN, that is embedded in PePSSI and can also be run as a separate program [31]. We have shown that PePSSI-predicted structures of peptide complexes with HLA-A2 match well with X-ray data [30], and we also found that peptide binding affinity with HLA-A2 is positively correlated with peptide-MHC contacts and negatively correlated with the number of interfacial water molecules predicted by PePSSI [30].…”