High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

ABSTRACT: Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other met

SUBMITTER: Shao XM 

PROVIDER: S-EPMC7056596 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature