title: Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors.
abstract: G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor's three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of approximately 300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs.
authors.affiliation: Department of Pharmacology, UMDNJ-Robert Wood Johnson Medical School and UMDNJ Informatics Institute, 675 Hoes Lane, Piscataway, NJ 08854, USA.
authors.forename: Sandhya
authors.initials: S
authors.lastname: Kortagere
authors.forename: William J
authors.initials: WJ
authors.lastname: Welsh
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001665:Binding Sites; D002417:Cattle; D017076:Computer-Aided Design; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D008958:Models, Molecular; D008969:Molecular Sequence Data; D051381:Rats; D017448:Receptors, Dopamine D2 / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism*; D012243:Rhodopsin / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D012985:Software Design
chemical_list: D008024:Ligands; D017448:Receptors, Dopamine D2; D043562:Receptors, G-Protein-Coupled; D012243:Rhodopsin