Computational methods for antibody-antigen complex structure prediction

Antibodies are key immune system proteins that recognize and neutralize antigens through their variable domains, which contain complementarity-determining regions (CDRs). Knowledge of antibody-antigen structures is essential for understanding immune recognition and guiding therapeutic antibody development. High-resolution experimental methods such as X-ray crystallography, NMR, and cryo-EM are resource-intensive and impractical for all antibodies due to the vast diversity of antigen-specific antibodies. As a result, computational structure prediction methods have become increasingly valuable for modeling antibody-antigen complexes. This study provides an overview of commonly used computational approaches for predicting antibody-antigen structures, including molecular docking and recent machine learning-driven approaches.