Dynamic Scoring: A novel method for quantitative modeling of guest-host associati

动态评分：宾主关联定量建模的新方法

• 批准号: 7510816
• 负责人: Markus Alexander Lill
• 金额: $ 19.72万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7510816
• 关键词: Address; Affinity; Algorithms; Area; Binding; Binding Proteins; Biological; Chemicals; Computer software; Computing Methodologies; Development; Docking; Drug Design; Drug or Chemical; Free Energy; Goals; Libraries; Ligand Binding; Ligands; Methods; Modeling; Pharmaceutical Preparations; Play; Pliability; Procedures; Process; Property; Protein Conformation; Protein Dynamics; Proteins; Public Health; Research Personnel; Role; Sampling; Scanning; Score; Screening procedure; Shapes; Simulate; Staging; Standards of Weights and Measures; System; Thinking; base; computerized tools; concept; environmental chemical; environmental toxicology; interest; molecular dynamics; molecular recognition; novel; pharmacophore; protein protein interaction; simulation; structural biology; tool; virtual

DESCRIPTION (provided by applicant): The goal of this proposal is to develop a new computational method to efficiently quantify protein-ligand association in a way that explicitly considers protein flexibility. Molecular recognition between molecules through noncovalent association plays a fundamental role in virtually all processes in biological systems. Although many computational concepts exist to simulate drug-protein recognition, an efficient and accurate quantification of these interactions has still not been achieved. We propose a novel computational method that addresses some of the most serious shortcomings of present approaches: protein flexibility and a reliable quantification of binding affinities. We introduce the new concept of a hypothetical 'ligand model': a virtual ligand that binds to the protein and dynamically changes its shape and properties during molecular dynamics (MD) simulations, essentially representing a large ensemble of different chemical species binding to the same target protein. This approach allows sampling protein conformations relevant to its interaction with chemicals or drug candidates. This method also will allow us to probe conformational flexibility of the protein upon ligand binding. The 'ligand-model' concept will result in an efficient decoupling of sampling using MD simulations and subsequent docking. This method consequently combines both accuracy in quantifying molecular recognition and efficiency in virtual screening of large compound libraries. The software is anticipated to be of wide interest for researchers in all areas of protein-ligand interactions, including drug design, structural biology, and environmental toxicology. PUBLIC HEALTH RELEVANCE Molecular recognition between molecules through noncovalent association plays a fundamental role in virtually all processes in biological systems. This project is aimed toward developing a novel computational method to efficiently quantify protein-ligand binding, explicitly including the dynamics of the protein. It will have wide applicability for drug design and environmental toxicology.

描述（由申请人提供）：该提案的目的是开发一种新的计算方法，以明确考虑蛋白质灵活性的方式有效地量化蛋白质 - 配体关联。通过非共价关联​​分子之间的分子识别在几乎所有过程中的生物系统过程中都起着基本作用。尽管存在许多计算概念来模拟药物蛋白识别，但仍未实现对这些相互作用的有效量化的量化。我们提出了一种新的计算方法，该方法解决了当前方法中一些最严重的缺点：蛋白质灵活性和可靠的结合亲和力量化。我们介绍了一个假设的“配体模型”的新概念：一种虚拟配体，该虚拟配体与蛋白质结合并在分子动力学（MD）模拟过程中动态改变其形状和特性，实质上代表了与同一靶蛋白结合的不同化学物种的大集合。这种方法允许对与化学物质或候选药物相互作用相关的采样蛋白构象。该方法还将使我们能够在配体结合时探测蛋白质的构象柔韧性。 “配体模型”概念将通过MD模拟和随后的对接进行有效地取消采样。因此，该方法结合了量化分子识别和效率的精度，在大型化合物文库的虚拟筛选中。预计该软件在蛋白质 - 配体相互作用的所有领域（包括药物设计，结构生物学和环境毒理学）的研究人员都具有广泛的兴趣。 通过非共价关联​​分子之间的公共卫生相关性分子识别在几乎所有过程的生物系统过程中都起着基本作用。该项目旨在开发一种新型的计算方法，以有效地量化蛋白质 - 配体结合，明确包括蛋白质的动力学。它将对药物设计和环境毒理学具有广泛的适用性。