An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

• DOI: 10.1016/j.str.2021.01.005
• Publication Date: 2021-02-05T16:30:16Z
• Authors: Johnathan D. Guest; Thom Vreven; Jing Zhou; Iain Moal; Jeliazko R. Jelia...; Jeffrey J. Gray; Zhiping Weng; Brian G. Pierce
• Keywords: 0301 basic medicine; Bioinformatics; Protein Conformation; Antigen-Antibody Complex; protein-protein docking; Antibodies, Viral; Antibodies; Structure-Activity Relationship; 03 medical and health sciences; Structural Biology; biotherapeutics; viruses; Amino Acids; Antigens; 0303 health sciences; and Proteins; Antibodies, Monoclonal; Computational Biology; affinity prediction; antibody design; Single-Domain Antibodies; 3. Good health; Molecular Docking Simulation; nanobody; Benchmarking; monoclonal antibodies; Peptides; Algorithms; Broadly Neutralizing Antibodies; Software; Protein Binding

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.