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

描述(由申请人提供)：这项提议的目标是开发一种新的计算方法，以一种明确考虑蛋白质灵活性的方式有效地量化蛋白质-配体结合。分子间通过非共价缔合的分子识别在生物系统中几乎所有的过程中都起着重要的作用。虽然存在许多计算概念来模拟药物-蛋白质识别，但仍然没有实现对这些相互作用的有效和准确的量化。我们提出了一种新的计算方法，解决了目前方法的一些最严重的缺点：蛋白质的灵活性和结合亲和力的可靠量化。我们引入了假设的“配体模型”的新概念：一个虚拟的配体，它与蛋白质结合，并在分子动力学(MD)模拟中动态改变其形状和性质，本质上代表了与同一目标蛋白质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蛋白质构象进行采样。这种方法还将使我们能够探索蛋白质在配体结合时的构象灵活性。“配基模型”的概念将导致使用MD模拟和随后的对接有效地分离采样。因此，这种方法结合了量化分子识别的准确性和虚拟筛选大型化合物文库的效率。预计该软件将在蛋白质-配体相互作用的所有领域引起研究人员的广泛兴趣，包括药物设计、结构生物学和环境毒理学。 公共卫生相关性分子之间通过非共价缔合进行的分子识别在生物系统中几乎所有的过程中都扮演着重要的角色。这个项目的目的是开发一种新的计算方法来有效地量化蛋白质与配体的结合，明确包括蛋白质的动力学。它将在药物设计和环境毒理学方面具有广泛的适用性。

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.