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dc.contributor.authorZhang, Yang
dc.date.accessioned2016-03-16T12:53:32Z
dc.date.available2016-03-16T12:53:32Z
dc.date.issued2016-01-26
dc.identifier.urihttp://hdl.handle.net/10754/601409
dc.description.abstractG protein-coupled receptors (or GPCRs) are integral transmembrane proteins responsible to various cellular signal transductions. Human GPCR proteins are encoded by 5% of human genes but account for the targets of 40% of the FDA approved drugs. Due to difficulties in crystallization, experimental structure determination remains extremely difficult for human GPCRs, which have been a major barrier in modern structure-based drug discovery. We proposed a new hybrid protocol, GPCR-I-TASSER, to construct GPCR structure models by integrating experimental mutagenesis data with ab initio transmembrane-helix assembly simulations, assisted by the predicted transmembrane-helix interaction networks. The method was tested in recent community-wide GPCRDock experiments and constructed models with a root mean square deviation 1.26 Å for Dopamine-3 and 2.08 Å for Chemokine-4 receptors in the transmembrane domain regions, which were significantly closer to the native than the best templates available in the PDB. GPCR-I-TASSER has been applied to model all 1,026 putative GPCRs in the human genome, where 923 are found to have correct folds based on the confidence score analysis and mutagenesis data comparison. The successfully modeled GPCRs contain many pharmaceutically important families that do not have previously solved structures, including Trace amine, Prostanoids, Releasing hormones, Melanocortins, Vasopressin and Neuropeptide Y receptors. All the human GPCR models have been made publicly available through the GPCR-HGmod database at http://zhanglab.ccmb.med.umich.edu/GPCR-HGmod/ The results demonstrate new progress on genome-wide structure modeling of transmembrane proteins which should bring useful impact on the effort of GPCR-targeted drug discovery.
dc.titleModeling structure of G protein-coupled receptors in huan genome
dc.typePresentation
dc.conference.dateJanuary 25-27, 2016
dc.conference.nameKAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology
dc.conference.locationKAUST, Thuwal, Saudi Arabia
dc.contributor.institutionDepartment of Computational Medicine and Bioinformatics, Department of Biological Chemistry, University of Michigan


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