Predicting MHC-peptide binding affinity by differential boundary tree.

ABSTRACT:

Motivation

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.

Results

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 algori

SUBMITTER: Feng P 

PROVIDER: S-EPMC8275335 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature