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

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
DOI: 10.1016/j.str.2021.01.005 Publication Date: 2021-02-05T16:30:16Z
ABSTRACT
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.
SUPPLEMENTAL MATERIAL
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