title: Pruned receptor surface models and pharmacophores for three-dimensional database searching.
abstract: A pharmacophore represents the 3D arrangement of chemical features that are shared by molecules exhibiting activity at a protein receptor. Pharmacophores are routinely used in 3D database searching for identifying potential lead compounds. The lack of shape constraints causes the query to identify compounds that could not fit into the active site. In the absence of structural information, a receptor surface model (RSM) can be used to represent the active site. The RSM consists of a surface that envelops a set of known actives after these have been aligned using their common features. When used for database searching, a RSM is overconstraining as it restricts access to regions that could be occupied by ligands, such as the solvent-protein interface or unexplored pockets. We describe a protocol for developing pruned RSMs using information gleaned from 3D quantitative structure-activity relationship (QSAR) models. We examined the performance of queries that consist of pharmacophores used alone or with pruned or unpruned RSMs by performing searches on six databases containing known actives distributed among inactives. The pruned RSMs yield an average selectivity 1.8 times greater than that for pharmacophore queries, compared to 1.6 times for unpruned RSMs. However, the pruned RSMs retrieve on average 73% of the actives identified using the pharmacophores, compared to 40% for the unpruned RSMs. As such, pruned RSMs represent a useful compromise between the high sensitivity of pharmacophores and the high selectivity of unpruned RSMs.
authors.affiliation: Departments of Chemistry and Pathology, Queen's University, Kingston, Ontario K7L 3N6, Canada.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Sutherland
authors.forename: Lee A
authors.initials: LA
authors.lastname: O'Brien
authors.forename: Donald F
authors.initials: DF
authors.lastname: Weaver
mesh_terms: D000578:Amidines / Q000737:chemistry; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000737:chemistry; D001569:Benzodiazepines; D000068579:Celecoxib; D002800:Cholinesterase Inhibitors / Q000737:chemistry; D051546:Cyclooxygenase 2; D016208:Databases, Factual; D000077265:Donepezil; D004656:Enalapril / Q000737:chemistry; D004798:Enzymes / Q000737:chemistry*; D004958:Estradiol / Q000737:chemistry; D007093:Imidazoles; D007189:Indans; D007527:Isoenzymes / Q000037:antagonists & inhibitors / Q000737:chemistry; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D009281:Naphthalenes / Q000737:chemistry; D010880:Piperidines / Q000737:chemistry; D011451:Prostaglandin-Endoperoxide Synthases / Q000737:chemistry; D011720:Pyrazoles; D021281:Quantitative Structure-Activity Relationship; D011956:Receptors, Cell Surface / Q000737:chemistry*; D018160:Receptors, Cytoplasmic and Nuclear / Q000737:chemistry*; D011960:Receptors, Estrogen / Q000737:chemistry; D011963:Receptors, GABA-A / Q000737:chemistry; D013449:Sulfonamides / Q000737:chemistry; D013917:Thrombin / Q000037:antagonists & inhibitors / Q000737:chemistry
chemical_list: D000578:Amidines; D000806:Angiotensin-Converting Enzyme Inhibitors; D002800:Cholinesterase Inhibitors; D004798:Enzymes; D007093:Imidazoles; D007189:Indans; D007527:Isoenzymes; D008024:Ligands; D009281:Naphthalenes; D010880:Piperidines; D011720:Pyrazoles; D011956:Receptors, Cell Surface; D018160:Receptors, Cytoplasmic and Nuclear; D011960:Receptors, Estrogen; D011963:Receptors, GABA-A; D013449:Sulfonamides; D001569:Benzodiazepines; D004958:Estradiol; D004656:Enalapril; C070567:N(alpha)-(4-toluenesulfonyl)-4-amidinophenylalanylpiperidine; C096580:Ro 14-5974; D000077265:Donepezil; D051546:Cyclooxygenase 2; D011451:Prostaglandin-Endoperoxide Synthases; D013917:Thrombin; D000068579:Celecoxib