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dc.contributor.authorGoel, Himanshu
dc.contributor.authorHazel, Anthony
dc.contributor.authorUstach, Vincent D.
dc.contributor.authorJo, Sunhwan
dc.contributor.authorYu, Wenbo
dc.contributor.authorMacKerell, Alexander D.
dc.date.accessioned2021-07-12T12:09:03Z
dc.date.available2021-07-12T12:09:03Z
dc.date.issued2021-05-25
dc.identifier.urihttp://hdl.handle.net/10713/16157
dc.description.abstractPredicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wanget al.(J. Am. Chem. Soc., 2015,137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wanget al.In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wanget al.and other published works. Overall, the SILCS methodology yields similar or better-quality predictions withouta priorineed for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery. © The Royal Society of Chemistry 2021.en_US
dc.description.sponsorshipNational Institutes of Healthen_US
dc.description.urihttps://doi.org/10.1039/d1sc01781ken_US
dc.language.isoenen_US
dc.publisherRoyal Society of Chemistryen_US
dc.relation.ispartofChemical Scienceen_US
dc.subjectfree energy perurbation (FEP)en_US
dc.subjectprotein-ligand binding affinitiesen_US
dc.subjectsite identification by ligand competitive saturation (SILCS)en_US
dc.subject.meshDrug Discoveryen_US
dc.titleRapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturationen_US
dc.typeArticleen_US
dc.identifier.doi10.1039/d1sc01781k
dc.source.volume12
dc.source.issue25
dc.source.beginpage8844
dc.source.endpage8858


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