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.
G-protein-coupled receptors (GPCRs) are a large and functionally diverse protein superfamily, which form a seven transmembrane (TM) helices bundle with alternating extra-cellular and intracellular loops. GPCRs are considered to be one of the most important groups of drug targets because they are involved in a broad range of body functions and processes and are related to major diseases. In this paper we present a new technology, named PREDICT, for modeling the 3D structure of any GPCR from its amino acid sequence. This approach takes into account both internal protein properties (i.e., the amino acid sequence) and the properties of the membrane environment. Unlike competing approaches, the new technology does not rely on the single known structure of rhodopsin, and is thus capable of predicting novel GPCR conformations. We demonstrate the capabilities of PREDICT in reproducing the known experimental structure of rhodopsin. In principle, PREDICT-generated models offer new opportunities for structure-based drug discovery towards GPCR targets.
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