AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction.

ABSTRACT: Antibodies play a key role in medical diagnostics and therapeutics. Accurately predicting antibody-antigen binding is essential for developing effective treatments. Traditional protein-protein interaction prediction methods often fall short because they do not account for the unique structural and dynamic properties of antibodies and antigens. In this study, we present AntiBinder, a novel predictive model specifically designed to address these challenges. AntiBinder integrates the unique structural and sequence characteristics of antibodies and antigens into its framework and employs a bidirectional cross-attention mechanism to automatically learn the intrinsic mechanisms of antigen-antibody binding, eliminating the need for manual feature engineering. Our comprehensive experiments, which

SUBMITTER: Zhang K 

PROVIDER: S-EPMC11744619 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature