Antibody structure prediction using interpretable deep learning.

ABSTRACT: Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, a deep learning method for predicting accurate antibody F_V structures from sequence. We evaluate DeepAb on a set of structurally diverse, therapeutically relevant antibodies and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as "black boxes" and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show our network attends to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions). Fina

SUBMITTER: Ruffolo JA 

PROVIDER: S-EPMC8848015 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature