Atomic-level, large-scale structure prediction of G protein-coupled receptors

G蛋白偶联受体的原子水平大规模结构预测

• 批准号: 7816746
• 负责人: Yang Zhang
• 金额: $ 31.82万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7816746
• 关键词: Acetylcholine; Adrenergic Agents; Adrenergic Receptor; Algorithms; Amino Acid Sequence; Architecture; Bacteriorhodopsins; Benchmarking; Biological Process; Biology; Cattle; Cell Surface Receptors; Chemicals; Collaborations; Communities; Computational algorithm; Computers; Computing Methodologies; Data; Databases; Development; Disease; Dopamine; Drug Delivery Systems; Drug Design; Eukaryotic Cell; Family; Feedback; G-Protein-Coupled Receptors; GTP-Binding Proteins; Generations; Goals; Histamine; Human; Integral Membrane Protein; Knowledge; Length; Ligand Binding; Ligands; Literature; Medical; Membrane Proteins; Methodology; Methods; Modeling; Molecular; Muscarinic Acetylcholine Receptor; Muscarinics; Mutagenesis; Pharmaceutical Preparations; Pharmacologic Substance; Physics; Physiological; Plant Roots; Preclinical Drug Evaluation; Procedures; Protein Family; Proteins; Proteome; Proteomics; Public Health; Relative (related person); Resolution; Rhodopsin; Role; Scientist; Screening procedure; Side; Signal Transduction; Site-Directed Mutagenesis; Speed; Staging; Structure; Testing; Validation; Variant; Vertebral column; Weight; adrenergic; base; chemokine; database structure; design; drug discovery; extracellular; fighting; human CCXCR1 receptor; improved; knowledge base; member; models and simulation; protein activation; protein structure; public health relevance; receptor; repository; research study; response; restraint; success; three dimensional structure; user-friendly; virtual

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

描述(申请人提供)：G蛋白偶联受体(GPCRs)是最大的整合膜蛋白家族，存在于几乎所有真核细胞中，将细胞外信号(配体结合)转化为细胞内信号(G蛋白激活)。这种重要的生理作用使它们成为最重要的药物靶标，约占当今现代药物的一半。显然，GPCRs的3D结构将为阐明分子组织和有效地虚拟筛选药物数据库提供必要的原子水平信息。然而，除了最近解决的人类β2-雄激素能受体外，还不可能获得其他人类GPCRs的实验结构信息。在线程装配精化(TASSER)算法用于低水平GPCR建模的最近成功的基础上，该提议寻求开发新的计算方法来生成经实验验证的原子级GPCR模型。重点将放在五个药学上重要的家族上，包括肾上腺素、趋化因子、多巴胺、组胺和毒扁豆碱乙酰胆碱。该项目的具体目标是：(1)开发和基准一种新的用于原子级GPCR蛋白质结构建模的GPCR-Tasser算法。(2)复合原子势和还原GPCR势的开发和优化。(3)推广GPCR-Tasser算法，以供公众使用和检验。(4)GPCRtasser在药学上重要的GPCRs中的应用。(5)与实验合作者一起对GPCR模型进行验证和改进。长期目标是(A)开发一套用于自动化和原子级GPCRs结构预测的计算机算法(B)将该方法扩展到对UniProt数据库中所有GPCRs的蛋白质组规模的结构建模(C)为公众可访问的GPCRs和结构数据库构建中央储存库，旨在最终缓解生物学和医学界对详细的原子GPCRs结构的迫切需求。与公共健康相关：在现代基于结构的药物设计中，科学家利用与特定疾病相关的蛋白质靶标的详细三维结构知识来设计抗击疾病的合成化合物。超过一半的药物靶标是G蛋白偶联受体(GPCRs)，这是一类结构极难通过实验获得的蛋白质。能够产生高分辨率GPCR结构的计算机算法的发展将加快对可能的化合物的初步筛选，从而对新药开发和公共卫生领域产生重要影响。