BERTMHC: improved MHC-peptide class II interaction prediction with transformer and multiple instance learning.

ABSTRACT:

Motivation

Increasingly comprehensive characterization of cancer-associated genetic alterations has paved the way for the development of highly specific therapeutic vaccines. Predicting precisely the binding and presentation of peptides to major histocompatibility complex (MHC) alleles is an important step toward such therapies. Recent data suggest that presentation of both class I and II epitopes are critical for the induction of a sustained effective immune response. However, the prediction performance for MHC class II has been limited compared to class I.

Results

We present a transformer neural network model which leverages self-supervised pretraining from a large corpus of protein sequences. We also propose a multiple instance learning (MIL) framework to deconvolve mass

SUBMITTER: Cheng J 

PROVIDER: S-EPMC9502151 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature