Scoring functions and enrichment: a case study on Hsp90
1 Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge CB2 1EW, UK
2 Arrow Therapeutics Ltd., Britannia House, 7 Trinity Street, London, SE1 1DA, UK
BMC Bioinformatics 2007, 8:27 doi:10.1186/1471-2105-8-27Published: 26 January 2007
The need for fast and accurate scoring functions has been driven by the increased use of in silico virtual screening twinned with high-throughput screening as a method to rapidly identify potential candidates in the early stages of drug development. We examine the ability of some the most common scoring functions (GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus) to discriminate correctly and efficiently between active and non-active compounds among a library of ~3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90).
Firstly, we investigated two ranking methodologies, GOLDrank and BestScorerank. GOLDrank is based on ranks generated using GOLD. The various scoring functions, GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus, are applied to the pose ranked number one by GOLD for that ligand. BestScorerank uses multiple poses for each ligand and independently chooses the best ranked pose of the ligand according to each different scoring function. Secondly, we considered the effect of introducing the Thr184 hydrogen bond tether to guide the docking process towards a particular solution, and its effect on enrichment. Thirdly, we considered normalisation to account for the known bias of scoring functions to select larger molecules. All the scoring functions gave fairly similar enrichments, with the exception of PMF which was consistently the poorest performer. In most cases, GOLD was marginally the best performing individual function; the Consensus score usually performed similarly to the best single scoring function. Our best results were obtained using the Thr184 tether in combination with the BestScorerank protocol and normalisation for molecular weight. For that particular combination, DOCK was the best individual function; DOCK recovered 90% of the actives in the top 10% of the ranked list; Consensus similarly recovered 89% of the actives in its top 10%.
Overall, we demonstrate the validity of virtual screening as a method for identifying new leads from a pool of ligands with similar physicochemical properties and we believe that the outcome of this study provides useful insight into the setting up of a suitable docking and scoring protocol, resulting in enrichment of 'target active' compounds.