ACCURATE PREDICTION OF BINDING AFFINITIES OF LIGANDS ACROSS THE MATRIX METALLOP

准确预测基质金属中配体的结合亲和力

• 批准号: 8364345
• 负责人: STEFAN BALAZ
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364345
• 关键词: Affinity; Binding; Binding Sites; Biomedical Research; Data; Development; Docking; Drug Design; Exhibits; Family; Funding; Grant; High Performance Computing; Isoenzymes; Ligand Binding; Ligands; Matrix Metalloproteinases; Metalloproteins; Methodology; Methods; Modeling; National Center for Research Resources; Outcome; Principal Investigator; Protein Family; Proteins; Publishing; Quantum Mechanics; Research; Research Infrastructure; Resources; Source; Structure; Testing; Time; United States National Institutes of Health; base; cost; member; molecular dynamics; molecular mechanics; response; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We request computational time and storage space needed for the development of an approach to predict binding affinities of ligands binding to any member of matrix metalloproteinase (MMP) family. MMPs exhibit high sequence identity, while having subtle differences in the binding sites. We hypothesize that the coefficients in the quantum mechanics/molecular mechanics (QM/MM) linear response (LR) models optimized for one MMP can be used with sufficient precision in the models for other MMPs. The hypothesis will be tested by developing and comparison of the QM/MM LR models for published binding data for several MMPs. The four tier approach1 for binding affinity calculation, developed in our lab, using (1) docking, (2) geometry optimization by QM/MM, (3) molecular dynamics (MD) simulation and (4) single point QM/MM energy calculations for the time-averaged structures from MD simulation was found to explain the experimental binding affinities more accurately than conventional methods, especially for metalloprotein ligands. The outcome of this project will enable us to decide whether the QM/MM LR approach can be extrapolated to other members of a particular protein family for accurate prediction of binding affinities based on subtle differences in the binding site residues. The results may allow us to apply this methodology to other protein families of multiple isoenzymes, leading to a tool for developing selective ligands to closely related proteins, with applications in drug design and similar fields.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 子项目的主要研究者可能是由其他来源提供的， 包括其他NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 我们要求的计算时间和存储空间所需的发展的方法来预测结合任何成员的基质金属蛋白酶（MMP）家族的配体的结合亲和力。MMP表现出高序列同一性，同时在结合位点上具有细微差异。我们假设，在量子力学/分子力学（QM/MM）的线性响应（LR）模型优化一个MMP的系数可以使用足够的精度在其他MMP的模型。将通过开发和比较几种MMP的已发表结合数据的QM/MM LR模型来检验该假设。结合亲和力计算的四层方法1，在我们的实验室，使用（1）对接，（2）几何优化的QM/MM，（3）分子动力学（MD）模拟和（4）单点QM/MM能量计算的时间平均结构的MD模拟被发现比传统的方法更准确地解释实验结合亲和力，特别是对金属蛋白配体。该项目的结果将使我们能够决定QM/MM LR方法是否可以外推到一个特定的蛋白质家族的其他成员的结合亲和力的基础上结合位点残基的细微差异的准确预测。这些结果可能使我们能够将这种方法应用于多种同工酶的其他蛋白质家族，从而为开发密切相关蛋白质的选择性配体提供工具，并在药物设计和类似领域中应用。