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dc.contributor.authorKonstantinou-Kirtay, Chrysi
dc.contributor.authorMitchell, John Blayney Owen
dc.contributor.authorLumley, James A.
dc.identifier.citationKonstantinou-Kirtay , C , Mitchell , J B O & Lumley , J A 2007 , ' Scoring functions and enrichment : a case study on Hsp90 ' , BMC Bioinformatics , vol. 8 , 27 .
dc.identifier.otherPURE: 3442511
dc.identifier.otherPURE UUID: 6002d7b4-07f7-4e7c-9211-5826ef38f33b
dc.identifier.otherWOS: 000244014500001
dc.identifier.otherScopus: 33846996344
dc.identifier.otherORCID: /0000-0002-0379-6097/work/34033413
dc.descriptionThis work was funded by the EPSRC, InsightFaraday (now part of the Chemistry Innovation Knowledge Transfer Network), Arrow Therapeutics Ltd and Unilever plc.en
dc.description.abstractBackground: 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 similar to 3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90). Results: Firstly, we investigated two ranking methodologies, GOLD(rank) and BestScore(rank). GOLD(rank) 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. BestScore(rank) 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 BestScore(rank) 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%. Conclusion: 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.
dc.relation.ispartofBMC Bioinformaticsen
dc.rights© 2007 Konstantinou-Kirtay et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectProtein-ligand interactionsen
dc.subjectMean forceen
dc.subjectChemical databasesen
dc.subjectMolecular dockingen
dc.subjectQD Chemistryen
dc.titleScoring functions and enrichment : a case study on Hsp90en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. EaSTCHEMen
dc.description.statusPeer revieweden

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