5712 J. Med. Chem. 2009, 52, 5712–5720 DOI: 10.1021/jm9006966 Automated Docking Screens: A Feasibility Study John J. Irwin,*,† Brian K. Shoichet,† Michael M. Mysinger,† Niu Huang,‡ Francesco Colizzi,§ Pascal Wassam,† and Yiqun Cao ) †Department of Pharmaceutical Chemistry, Byers Hall, Box 2550, University of California San Francisco, San Francisco, California 94158-2330, ‡National Institute of Biological Sciences (NIBS), Beijing, No. 7 Science Park Road, Zhongguancun Life Science Park, Changping District, Beijing 102206, P. R. China, §Dipartimento di Scienze Farmaceutiche, Universita di Bologna, Via Belmeloro 6, 33, 40126 Bologna, Italy, and D)epartment of Computer Science and Engineering, University of California, Riverside, California 92521 Received May 22, 2009 Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 A˚ rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 A˚ rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at http://blaster.docking.org. Introduction Molecular docking has had important recent successes1-12 and is now widely used in industry and academia. But whereas other techniques in computational biology such as homology modeling13 and sequence database searching14 have been successfully deployed on a proteomic scale, docking has remained manually intensive. Docking programs are challen- ging to use, with many parameters to be chosen, file formats to be manipulated, and decisions to be made at both the pre- paration and analysis stages. Even in expert hands, there are targets for which docking simply fails to recapitulate experi- mentally known binding information. These barriers to entry have diminished the impact of the technique by making it less accessible to biologically oriented nonexperts and challenging even for specialists to deploy on a large scale. One approach to make docking accessible to more investi- gators, and to make it more systematic even for experts, is to automate it. We have therefore investigated an expert system, DOCK Blaster, which aims to emulate experts at all stages of the docking process. Ideally, DOCK Blaster could start from as little as a PDBa code and from that launch a full screen of a large compound library to find novel ligands. To do so it must overcome substantial challenges in preparing a target site for docking, it must explore variation in the sampling and often scoring, and it must conduct control calculations to judge the quality of the screen. For instance, the automated procedure must recognize common cofactors, metals, post-translational *To whom correspondence should be addressed. Phone: 415-514- 4127. Fax: 415-514-4260. E-mail: jji@cgl.ucsf.edu. a Abbreviations: rmsd, root mean squared deviation; SAR, structure- activity relationship; PDB, Protein Data Bank; ROC, receiver operator characteristic. pubs.acs.org/jmc Published on Web 08/31/2009 modifications, and solutes to identify the ligand and separate it from the receptor. Second, “hot spots” for docking must be identified for a wide range of binding site sizes and shapes. Third, parameters must be assigned to receptor atoms in a robust way that can cope not only with cofactors, metals, and post-translational modifications but also with unforeseen moieties for which no dictionary is available. Because an expert would normally experiment with several variations in sampling and scoring, an automated system should do so also, picking the best parameters to use for a full database screen. Finally, the entire docking process should be integrated from end to end so as to recover from simple problems, continue as far as possible, and end gracefully should an unrecoverable error occur. Here we describe DOCK Blaster, a fully automated dock- ing system including self-assessment. The method is tested for pose-fidelity, the ability to reproduce experimentally observed poses within some tolerance limit, and enrichment, the ability to enrich actives from among a database of decoys, where a decoy is a member of the database that does not bind to the target. We have used three of the most common benchmark- ing sets: the Astex-85 set,7 having 85 high-quality crystal structures of therapeutically relevant targets and “drug-like” ligands, the GOLD-114 benchmark,15 derived from the most widely used docking benchmarks,16,17 and the DUD set,27 38 protein targets for which sets of annotated actives and corre- sponding property-matched decoys are available for each target. Property-matched decoys have similar physical prop- erties but different topologies that one would not expect should be recognized by the protein, a key requirement for a high score. Whereas we find that the automated method is typically outperformed by an expert, its performance is never- theless respectable. r 2009 American Chemical Society