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

基本信息

批准号：
8105073
负责人：
Yang Zhang
金额：
$ 31.59万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
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/8105073
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 Health 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 improved knowledge base member models and simulation protein activation protein structure receptor repository research study restraint success three dimensional structure user-friendly virtual web site

项目摘要

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（由申请人提供）：G蛋白偶联受体（GPCR）是最大的整体膜蛋白家族，几乎在每个真核细胞中发生，可将细胞外信号（配体结合）转导为细胞内信号（G蛋白活化）。这种基本的生理作用使它们成为最重要的药物靶标，占当今现代药物的大约一半。显然，GPCR的3D结构将提供必不可少的原子级信息，以阐明分子组织并有效地筛选药物数据库。但是，除了最近解决的人beta2 andrencrenagic受体外，尚未获得其他人GPCR的实验结构信息。该提案以降低级别的GPCR建模的螺纹组装（Tasser）算法的最新成功为基础，该建议旨在开发新的计算方法，以生成经过实验验证的原子级GPCR模型。重点将放在五个具有肾上腺素能，趋化因子，多巴胺，组胺和毒蕈碱乙酰胆碱的家庭上。该项目的具体目的是：（1）用于原子级GPCR蛋白结构建模的新GPCR-Tasser算法的开发和基准测试。（2）复合原子和降低的GPCR电位的发展和优化。（3）传播GPCR-Tasser算法供公众使用和检查。（4）将GPCR-TASSER应用于药品重要的GPCR。（5）通过实验合作者对GPCR模型的验证和完善。 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详细的原子GPCR结构。公共卫生相关性：在基于现代结构的药物设计中，科学家使用与特定疾病相关的蛋白质靶标的三维结构的详细知识，以设计与疾病作斗争的合成化合物。在所有药物靶标中，超过一半是G蛋白偶联受体（GPCR），这是一个蛋白质家族，其结构极难通过实验获得。能够产生高分辨率GPCR结构的基于计算机的算法的开发将加快推定化合物的初始筛查，因此对新药物发现和公共卫生的领域产生了重要影响。