AI-augmented physics-based docking for antibody-antigen complex prediction.

ABSTRACT:

Motivation

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens.

Methods

We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In th

SUBMITTER: Gaudreault F 

PROVIDER: S-EPMC11978387 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature