Predicting MHC-peptide binding affinity by differential boundary tree.

Abstract

The prediction of the binding between peptides and major histocompatibility complex (MHC) molecules plays an important role in neoantigen identification. Although a large number of computational methods have been developed to address this problem, they produce high false-positive rates in practical applications, since in most cases, a single residue mutation may largely alter the binding affinity of a peptide binding to MHC which cannot be identified by conventional deep learning methods. We developed a differential boundary tree-based model, named DBTpred, to address this problem. We demonstrated that DBTpred can accurately predict MHC class I binding affinity compared to the state-of-art deep learning methods. We also presented a parallel training algorithm to accelerate the training and inference process which enables DBTpred to be applied to large datasets. By investigating the statistical properties of differential boundary trees and the prediction paths to test samples, we revealed that DBTpred can provide an intuitive interpretation and possible hints in detecting important residue mutations that can largely influence binding affinity. The DBTpred package is implemented in Python and freely available at: https://github.com/fpy94/DBT. Supplementary data are available at Bioinformatics online.