  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
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 (CNN)-based methods named ConvM and 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 ConvM and SpConvM to better identify the most important
amino acids for the binding events. Our experimental results demonstrate that
our models significantly outperform the state-of-the-art methods including
MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on
ROC5 across 128 alleles.
