Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction

Abstract

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method $\mathrm{MHCflurry}$ , have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named $\mathrm{ConvM}$ and $\mathrm{SpConvM}$ to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in $\mathrm{ConvM}$ and $\mathrm{SpConvM}$ to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including $\mathrm{MHCflurry}$ with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.

Keywords: attention; convolutional neural networks; deep learning; peptide vaccine design; prioritization.