PREDICT modeling and in-silico screening for G-protein coupled receptors
| Autor: | Yael Marantz, Alexander Heifetz, Dora Warshaviak, Silvia Noiman, Boaz Inbal, Maya Topf, Merav Fichman, Oren M. Becker, Zvi Naor, Noa Avisar, Sharon Shacham, Ori Kalid, Shay Bar-Haim |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Models
Molecular Rhodopsin Protein Conformation In silico Computational biology Biology Ligands Biochemistry Protein Structure Secondary Receptors G-Protein-Coupled Protein structure Structural Biology Animals Combinatorial Chemistry Techniques Humans Computer Simulation Amino Acid Sequence Molecular Biology G protein-coupled receptor Virtual screening Binding Sites Receptors Dopamine D2 Drug discovery Stereoisomerism Receptors Neurokinin-1 Combinatorial chemistry Receptors Neuropeptide Y Transmembrane domain Models Chemical Docking (molecular) Drug Design biology.protein Thermodynamics Hydrophobic and Hydrophilic Interactions Monte Carlo Method Algorithms |
| Zdroj: | Proteins: Structure, Function, and Bioinformatics. 57:51-86 |
| ISSN: | 0887-3585 |
| DOI: | 10.1002/prot.20195 |
| Popis: | G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text