title: In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking.
abstract: Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.
authors.affiliation: Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, USA.
authors.forename: Erin S D
authors.initials: ES
authors.lastname: Bolstad
authors.forename: Amy C
authors.initials: AC
authors.lastname: Anderson
mesh_terms: D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D017124:Conserved Sequence; D055808:Drug Discovery / Q000379:methods; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry* / Q000378:metabolism
chemical_list: D008024:Ligands; D011506:Proteins; D013762:Tetrahydrofolate Dehydrogenase
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