G protein-coupled receptors (GPCRs) are the largest family of integral membrane proteins that occur in nearly every eukaryotic cell to transduce an extracellular signal (ligand binding) into an intracellular signal (G protein activation). This essential physiological role makes them the most important pharmaceutical targets which comprise approximately half of today's modern medicinal drugs. Clearly, 3D-structures of GPCRs would provide essential atomic-level information for elucidating the molecular organization and for efficient virtual screening of drug databases. However, except for the recently solved human beta2-andrenergic receptor, it has not yet been possible to obtain experimental structural information for other human GPCRs. Building on the recent success of the threading assemble refinement (TASSER) algorithm for reduced-level GPCR modeling, this proposal seeks to develop new computational methodologies for the generation of experiment- validated, atomic-level GPCR models. The focus will be on five pharmaceutically important families including Adrenergic, Chemokine, Dopamine, Histamine, and Muscarinic acetylcholine.
Specific aims of the project are: (1) Development and benchmarking of a new GPCR-TASSER algorithm for atomic-level GPCR protein structure modeling. (2) Development and optimization of composite atomic and reduced GPCR potentials. (3) Dissemination of GPCR-TASSER algorithm for public use and examination. (4) Application of GPCR-TASSER to the pharmaceutically important GPCRs. (5) Validation and refinement of the GPCR models with experiment collaborators. The long-term goals are (a) to develop a set of computer algorithms for automated and atomic- level GPCR structure prediction (b) to extend the methodology to proteomic-scale structure modeling for all GPCRs in UniProt database (c) to construct a central repository for publicly-accessible GPCR algorithms and structure databases which are designed to eventually alleviate the urgent need in biology and medical communities for the detailed atomic GPCR structures.
In modern structure-based drug design, scientists use detailed knowledge of the 3-dimensional structure of protein targets associated with particular diseases to design synthetic compounds that fight the disease. More than half of all drug targets are G protein-coupled receptors (GPCRs), a family of proteins whose structures are extremely difficult to obtain by experiment. The development of computer-based algorithms that are able to generate high resolution GPCR structures will speed up the initial screening of putative chemical compounds and therefore have an important impact on the field of the new drug discovery and public health.
|Quan, Shu; Wang, Lili; Petrotchenko, Evgeniy V et al. (2014) Super Spy variants implicate flexibility in chaperone action. Elife 3:e01584|
|Szilagyi, Andras; Zhang, Yang (2014) Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 24:10-23|
|Zhang, Yang (2014) Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10. Proteins 82 Suppl 2:175-87|
|Xu, Ying-Ying; Yang, Fan; Zhang, Yang et al. (2013) An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. Bioinformatics 29:2032-40|
|Yan, Renxiang; Xu, Dong; Yang, Jianyi et al. (2013) A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci Rep 3:2619|
|Xu, Dong; Zhang, Yang (2013) Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment. Sci Rep 3:1895|
|Abrusan, Gyorgy; Zhang, Yang; Szilagyi, Andras (2013) Structure prediction and analysis of DNA transposon and LINE retrotransposon proteins. J Biol Chem 288:16127-38|
|Abrusan, Gyorgy; Szilagyi, Andras; Zhang, Yang et al. (2013) Turning gold into 'junk': transposable elements utilize central proteins of cellular networks. Nucleic Acids Res 41:3190-200|
|Fu, Jiaqi; Ling, Song; Liu, Ying et al. (2013) A small shared epitope-mimetic compound potently accelerates osteoclast-mediated bone damage in autoimmune arthritis. J Immunol 191:2096-103|
|Yang, Jianyi; Roy, Ambrish; Zhang, Yang (2013) BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res 41:D1096-103|
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