Publication Date: 1/23/2023
Reference: 39(2):1-8, 2023
Authors: Ziqi Chen, NEC Laboratories America, Inc., The Ohio State University; Baoyi Zhang, Rice University; Hongyu Guo, National Research Council Canada; Prashant Emani, Yale University; Trevor Clancy, NEC OncoImmunity AS; Chongming Jiang, Baylor College of Medicine; Mark Gerstein, Yale University; Xia Ning, The Ohio State University; Chao Cheng, Baylor College of Medicine; Martin Renqiang Min, NEC Laboratories America, Inc.
Abstract: Motivation: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed.Results: We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods.
Publication Link: https://academic.oup.com/bioinformatics/article/39/2/btad055/7000336