title: SitePrint: three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification, and drug design.
abstract: Integrating biological and chemical information is one key task in drug discovery, and one approach to attaining this goal is via three-dimensional pharmacophore descriptors derived from protein binding sites. The SitePrint program generates, aligns, scores, and classifies three-dimensional pharmacophore descriptors, active site grids, and ligand surfaces. The descriptors are formed from molecular fragments that have been docked, minimized, filtered, and clustered in protein active sites. The descriptors have geometric coordinates derived from the fragment positions, and they capture the shape, electrostatics, locations, and angles of entry into pockets of the recognition sites: they also provide a direct link to databases of organic molecules. The descriptors have been shown to be robust with respect to small changes in protein structure observed when multiple compounds are cocrystallized in a protein. Five aligned thrombin cocrystals with an average core alpha-carbon RMSD of 0.7 A gave three-dimensional pharmacophore descriptors with an average RMSD of 1.1 A. On a larger test set, alignment and scoring of the descriptors using clique-based alignment, and a best first search strategy with an adapted forward-looking Ullmann heuristic was able to select the global minimum three-dimensional alignment in twenty-nine out of thirty cases in less than one CPU second on a workstation. A protein family based analysis was then performed to demonstrate the usefulness of the method in producing a correlation of active site pharmacophore descriptors to protein function. Each protein in a test set of thirty was assigned membership to a family based on computed active site similarity to the following families: kinases, nuclear receptors, the aspartyl, cysteine, serine, and metallo proteases. This method of classifying proteins is complementary to approaches based on sequence or fold homology. The values within protein families for correctly assigning membership of a protein to a family ranged from 25% to 80%.
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, Box 2240, N474-A Genentech Hall, San Francisco, California 94143-2240, USA.
authors.forename: James R
authors.initials: JR
authors.lastname: Arnold
authors.forename: Keith W
authors.initials: KW
authors.lastname: Burdick
authors.forename: Scott C-H
authors.initials: SC
authors.lastname: Pegg
authors.forename: Samuel
authors.initials: S
authors.lastname: Toba
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Lamb
authors.forename: Irwin D
authors.initials: ID
authors.lastname: Kuntz
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D016208:Databases, Factual; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins