The CASP 16 Experimental Protein-Ligand Datasets.

This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction experiment-the first CASP round to incorporate targets from pharmaceutical discovery projects. We have assembled and characterized protein-ligand complexes for four proteins that are known or candidate drug targets: human chymase, human cathepsin G, human autotaxin, and the SARS-CoV-2 main protease. The collection encompasses over 200 co-crystal structures at resolutions better than 2.7 Å, paired with binding affinity measurements for approximately 160 compounds covering a broad affinity range (nanomolar to high micromolar). These data enabled the CASP16 pose-prediction and affinity-prediction challenges. Many systems feature potentially challenging characteristics, including chymase's electropositive surface and acidic ligands, which require proper handling of titratable ligand groups; autotaxin complexes with and without zinc coordination; and a SARS-CoV-2 protease crystal form exhibiting an unusually open active site conformation. We describe the experimental approaches-from protein production and crystallization to binding assay development-that yielded these reference data. Contributed by scientists at F. Hoffmann-La Roche and Idorsia Pharmaceuticals, these datasets represent actual drug discovery projects and therefore provide a realistic testbed for assessing how computational methods perform on pharmaceutically relevant targets. An accompanying paper in the present special journal issue provides a comprehensive assessment of the pose and affinity predictions for these pharmaceutical protein-ligand systems.