Molecular Recognition of Proteins and Ligand Design

蛋白质的分子识别和配体设计

• 批准号: 7932631
• 负责人: William L. Jorgensen
• 金额: $ 2.47万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7932631
• 关键词: AIDS/HIV problem; Affinity; Algorithms; Amino Acid Sequence; Anti-HIV Agents; Area; Arthritis; Atherosclerosis; Bacterial Infections; Binding; Binding Proteins; Binding Sites; Biochemical; Biochemical Reaction; Biological Assay; Blood - brain barrier anatomy; CDK2 gene; Cells; Chemicals; Clinical; Complex; Computer Simulation; Computer software; Computing Methodologies; Coupled; DNA Gyrase; Data; Databases; Development; Disease; Drug Design; Enzymes; Free Energy; Freedom; Generations; Goals; Gram-Positive Bacterial Infections; Grant; HIV; Huntington Disease; Hydrolysis; Inflammatory; Investigation; Knowledge; Lead; Libraries; Ligands; Malignant Neoplasms; Mechanics; Methods; Migration Inhibitory Factor; Modeling; Molecular; Molecular Models; Molecular Structure; Multi-Drug Resistance; Neurologic; Parkinson Disease; Peptide Sequence Determination; Permeability; Pharmaceutical Preparations; Phosphotransferases; Property; Prostaglandin-Endoperoxide Synthase; Protein Binding; Protein Inhibition; Proteins; RNA-Directed DNA Polymerase; Reaction; Research; Rheumatoid Arthritis; Rotation; Sampling; Science; Sepsis; Series; Serum Proteins; Sleep Disorders; Solubility; Solutions; Solvents; Statistical Mechanics; Structure; System; Technology; Testing; Therapeutic; Therapeutic Agents; Training; Vancomycin; Variant; Vertebral column; Water; Work; analog; aqueous; base; biochemical evolution; cancer pain; combat; combinatorial; computerized tools; design; direct application; enzyme mechanism; fatty acid amide hydrolase; improved; inhibitor/antagonist; method development; model design; molecular mechanics; molecular modeling; molecular recognition; next generation; phenylpyruvate tautomerase; polypeptide; programs; protein folding; quantum; response; simulation; software systems; success; tool

DESCRIPTION (provided by applicant): The primary goal of the research is to develop and use computational tools to make quantitative predictions on the structure, reactivity and inhibition of proteins. Sophisticated software is being created for drug design with specific applications to cancer, arthritis, bacterial infections, sleep disorders, and Parkinson's disease. The research includes fundamental advances in the development of methods and software for modeling proteins and features applications on protein-ligand binding, inhibitor design, protein folding, and enzymatic reactions. Atomic-level computer simulations are the primary tool at three levels of complexity: a scoring function approach with the GenMol program, which can rapidly build combinatorial libraries of protein-ligand complexes, and extended linear response (ELR) and rigorous free-energy perturbation (FEP) calculations using Monte Carlo statistical mechanics (MC). The MC simulations are performed for the protein-ligand complexes in the presence of explicit water molecules and involve sampling of all degrees of freedom for the systems. The resultant detailed structural and energetic information helps elucidate variations in binding affinities as either the structure of the ligand or the protein sequence is modified. In turn, this knowledge forms the basis for the design of high-affinity, protein-selective ligands. In order to refine the predictive abilities of the methods, training and testing will use a database of ca. 2000 protein-ligand complexes with known activity data for analog series. Specific extensive studies of protein-ligand binding and drug-lead optimization are targeted for several proteins including CDK2 kinase, cyclooxygenases, DNA gyrase, and fatty acid amide hydrolase. The drug design is enhanced by application of the QikProp program, which analyzes the drug-likeness of input organic molecules through estimation of pharmaceutically relevant properties including aqueous solubility, cell permeabilities, blood-brain barrier permeability, and serum protein binding. Other activities are the development of an improved "force field" for the description of intra and inter-molecular energetics, examination of the mechanisms of enzymatic reactions, and prediction of the structures of polypeptides in aqueous solution using new MC sampling methods with a continuum solvent model.

描述（由申请人提供）：研究的主要目标是开发和使用计算工具对蛋白质的结构，反应性和抑制进行定量预测。正在为药物设计创建复杂的软件，这些软件具体应用于癌症、关节炎、细菌感染、睡眠障碍和帕金森氏病。该研究包括蛋白质建模方法和软件开发的基本进展，以及蛋白质-配体结合，抑制剂设计，蛋白质折叠和酶促反应的功能应用。原子级计算机模拟是三个复杂程度的主要工具：GenMol程序的评分函数方法，可以快速构建蛋白质-配体复合物的组合库，以及使用Monte Carlo统计力学（MC）的扩展线性响应（ELR）和严格自由能微扰（FEP）计算。MC模拟进行蛋白质-配体复合物在明确的水分子的存在下，并涉及采样的所有自由度的系统。所得的详细结构和能量信息有助于阐明结合亲和力的变化，因为配体或蛋白质序列的结构被修改。反过来，这些知识形成了设计高亲和力、蛋白质选择性配体的基础。为了改进方法的预测能力，训练和测试将使用ca数据库。2000蛋白质-配体复合物，具有已知的类似物系列活性数据。针对包括CDK 2激酶、环加氧酶、DNA促旋酶和脂肪酸酰胺水解酶在内的几种蛋白质，对蛋白质-配体结合和药物-先导物优化进行了具体的广泛研究。通过应用QikProp程序来增强药物设计，QikProp程序通过估计药学相关性质（包括水溶性、细胞渗透性、血脑屏障渗透性和血清蛋白结合）来分析输入有机分子的药物相似性。其他活动是一个改进的“力场”的内部和分子间的能量学的描述，酶促反应的机制的检查，并预测在水溶液中的多肽的结构，使用新的MC采样方法与连续溶剂模型的发展。