Drug Binding Free Energies with Implicit Solvent Methods

使用隐式溶剂方法的药物结合自由能

• 批准号: 7228984
• 负责人: Michael R Shirts
• 金额: $ 4.88万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7228984
• 关键词: Adopted; Algorithms; Binding; Binding Proteins; Complex; Computers; Computing Methodologies; Drug Design; Free Energy; Generations; Ligand Binding; Ligands; Measures; Medicine; Membrane Proteins; Methods; Modeling; Pharmaceutical Preparations; Proteins; Range; Research; Sampling; Scientist; Solvents; Standards of Weights and Measures; Statistical Methods; Structure; System; Techniques; cost; drug development; improved; molecular mechanics; simulation

DESCRIPTION (provided by applicant): 1) To take standard implicit solvent models, combined with atomistic biomolecular models and modern sampling techniques, and attempt to directly evaluate the absolute free energy of ligand binding in a range of protein-ligand systems. 2) To develop statistical methods to identify of which, if any, of various sampling techniques are able to truly able to yield precise free energies of binding complexes for a range of relevant systems. 3) To use these results to identify ways in which the implicit solvent and atomistic biomolecular models can be improved to better fit the binding energies and structures for a wide variety of ligand protein complexes. This research has important consequences for rational drug design, and consequently all of medicine. If the predictive ability of biomolecular simulations can be significantly improved, and the efficiency of these methods increased, it will result in significantly lowered total costs of drug development.

1）采用标准隐式溶剂模型，结合原子生物分子模型和现代采样技术，并试图直接评估一系列蛋白质-配体系统中配体结合的绝对自由能。2)开发统计方法，以确定各种采样技术中的哪些（如果有的话）能够真正产生一系列相关系统的结合复合物的精确自由能。3)使用这些结果，以确定隐含的溶剂和原子的生物分子模型，可以改善，以更好地适应各种各样的配体蛋白质复合物的结合能和结构的方式。这项研究对合理的药物设计，从而对所有的医学都有重要的影响。如果生物分子模拟的预测能力能够显著提高，并且这些方法的效率提高，则将导致药物开发的总成本显著降低。

项目成果

Multiscale Optimization of a Truncated Newton Minimization Algorithm and Application to Proteins and Protein-Ligand Complexes.

截断牛顿最小化算法的多尺度优化及其在蛋白质和蛋白质-配体复合物中的应用。

• DOI: 10.1021/ct600129f
• 发表时间: 2007
• 期刊: Journal of chemical theory and computation
• 影响因子: 5.5
• 作者: Zhu,Kai;Shirts,MichaelR;Friesner,RichardA;Jacobson,MatthewP
• 通讯作者: Jacobson,MatthewP