CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series
| Autor: | Heather A. Carlson, Jeanne A. Stuckey, James B. Dunbar, Richard D. Smith, Kelly L. Damm-Ganamet |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Research groups
Databases Pharmaceutical Protein Conformation Computer science General Chemical Engineering Library and Information Sciences Ligands Machine learning computer.software_genre 01 natural sciences Molecular Docking Simulation Article Structure-Activity Relationship 03 medical and health sciences Native contact Simulation 030304 developmental biology 0303 health sciences business.industry Proteins Experimental data General Chemistry 0104 chemical sciences Computer Science Applications Benchmarking 010404 medicinal & biomolecular chemistry Docking (molecular) Drug Design Pose prediction Artificial intelligence business computer |
| Zdroj: | Journal of Chemical Information and Modeling |
| ISSN: | 1549-960X 1549-9596 |
| DOI: | 10.1021/ci400025f |
| Popis: | The Community Structure–Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|