Affiliation:
1. Fresh Wind Biotechnologies Inc. (Tianjin) , Tianjin , China
2. Fresh Wind Biotechnologies USA Inc. , Houston, TX , USA
Abstract
Abstract
Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, ‘TCR Antigen Binding Recognition’ based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.
Funder
Fresh Wind Biotechnologies USA Inc.
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems