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

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.
Competing Interests: Declaration of interests I.M. is employed by GlaxoSmithKline plc, which discovers and sells antibody therapies. Z.W. is a cofounder of Rgenta Therapeutics and serves on its scientific advisory board. J.J.G. is an unpaid board member of the Rosetta Commons. Under institutional participation agreements between the University of Washington, acting on behalf of the Rosetta Commons, Johns Hopkins University may be entitled to a portion of revenue received on licensing Rosetta software, including some methods described in this article. As a member of the Scientific Advisory Board, J.J.G. has a financial interest in Cyrus Biotechnology. Cyrus Biotechnology distributes the Rosetta software, which includes methods described in this article. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies.
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