Revealing the Ligand Binding Landscape with Advanced Molecular Simulation Methods

利用先进的分子模拟方法揭示配体结合景观

• 批准号: 10166872
• 负责人: Alexander Dickson
• 金额: $ 33.49万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166872
• 关键词: Affect; Affinity; Algorithms; Binding; Biophysics; Cells; Chemicals; Cloning; Control Groups; Coupled; Development; Diffusion; Docking; Drug Design; Drug Kinetics; Drug Modelings; Drug Targeting; Epoxide hydrolase; Equilibrium; Event; Free Energy; Freedom; G-Protein-Coupled Receptors; Human body; Ingestion; Intake; Kinetics; Knowledge; Laboratories; Lead; Ligand Binding; Ligands; Metabolism; Methods; Modeling; Molecular; Molecular Conformation; Motion; Organism; Outcome; Pathway interactions; Pharmaceutical Preparations; Pharmacologic Substance; Process; Property; Protein Biosynthesis; Protein Dynamics; Proteins; Running; Sampling; Screening procedure; Series; Structural Models; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Uncertainty; Work; base; cell growth; computational pipelines; design; diabetic patient; driving force; drug action; drug discovery; drug efficacy; druggable target; empowered; experimental study; fitness; improved; inhibitor/antagonist; insight; molecular dynamics; novel; painful neuropathy; pharmacophore; protein degradation; residence; screening; simulation; success; time use; tool; virtual screening

Project Summary Human bodies are living systems that are constantly in flux. Pharmaceutical drugs take action within this non-equilibrium context: after a drug is ingested it is absorbed, distributed to tissues, bound (both on-target and off-target), released, metabolized and eliminated. Each of these processes occurs with a rate, and the efficacy of a drug is a largely function of these rates. In contrast, the dominant paradigm in drug discovery has been the optimization of affinity, which alone is insufficient to determine the rates of binding (kon) and unbinding (koff). Though the binding affinity is the ratio of the koff and kon, and longer residence times can lead to higher binding affinity, these are not well-correlated, as kon values can vary from diffusion-limited (109 M-1 s-1) down to <104 M-1 s-1 for protein targets with slow degrees of freedom, such G-protein-coupled receptors. Prediction of affinity is easier than kinetics as it is a state function, which depends only on the endpoints of the binding path. Binding kinetics are dependent on the molecular details encoded in the ligand binding transition state – the highest point in free energy along the binding pathway. Molecular dynamics simulation can be used to study these transition states in atomic detail, but only recently – empowered by advances in hardware and new algorithms for simulation – has it become capable of simulating unbiased ligand binding and release events, which can be coupled to long timescale protein motions. As such, little is known about the ligand binding transition state for a given protein target, and how it changes from ligand to ligand. Empowered by the WExplore enhanced sampling method (developed by the PI), the Dickson laboratory will use molecular dynamics simulation to reveal the landscape of protein-ligand conformations. WExplore can generate extremely rare ligand release pathway ensembles (events occurring only once in ~1000 seconds) without the use of biasing forces, which is a dramatic improvement upon current technology. Importantly, this will enable analysis of the ligand binding transition states for a series of ligands on two protein drug targets (soluble epoxide hydrolase (sEH), and Translocator protein 18kDA (TSPO)). This will mark the first study of the robustness of ligand binding transition states, which is a key quantity for kinetics-based drug design. Further, this work will build a method to encode properties of the transition state into screening tools that can, for the first time, screen ligands according to kinetics in a high-throughput manner. These methods will then be applied to identify new long residence time inhibitors for both sEH and TSPO, two systems where residence time has been shown to be important for drug efficacy.

项目摘要 人体是不断变化的生命系统。药品在以下方面采取行动 这种非平衡背景：药物被摄取后被吸收，分布到组织，结合(两者都在靶上 和偏离目标)、释放、代谢和消除。这些过程中的每一个都以一定的速率发生，并且 一种药物的疗效在很大程度上取决于这些比率。相比之下，药物发现中的主导范式 亲和力的优化，单靠亲和力不足以确定结合率(Kon)和解结率 (科夫)。虽然结合亲和力是Koff和Kon的比率，但停留时间越长，会导致更高的亲和力 结合亲和力，这些不是很好的相关性，因为kon值可以从扩散受限(109M-1 S-1)到 &lt；104M-1 S-1为具有慢自由度的蛋白质靶标，如G蛋白偶联受体。 亲和力的预测比动力学更容易，因为它是一个状态函数，只依赖于端点 绑定路径的。结合动力学取决于配体结合中编码的分子细节 过渡态--结合路径上自由能的最高点。分子动力学模拟 可以用来研究这些过渡态的原子细节，但只是在最近--在 用于模拟的硬件和新算法-它是否能够模拟无偏配体结合和 释放事件，这可以与长时间尺度的蛋白质运动相结合。因此，人们对此知之甚少 给定蛋白质靶标的配体结合过渡态，以及它如何在不同的配体之间变化。 在WExplore增强型抽样方法(由PI开发)的支持下，Dickson实验室 将使用分子动力学模拟来揭示蛋白质-配体构象的景观。WExplore可以 产生极其罕见的配体释放途径集合(事件在~1000秒内只发生一次) 没有使用偏向力，这是对当前技术的戏剧性改进。重要的是，这 将能够分析两个蛋白质药物靶标上的一系列配体的配体结合过渡态 (可溶性环氧化物水解酶(SEH)和转运蛋白18kDA(TSPO))。这将标志着对 配体结合过渡态的稳健性，这是基于动力学的药物设计的关键参数。 此外，这项工作将建立一种方法，将过渡态的属性编码到筛选工具中 这可以第一次以高通量的方式根据动力学筛选配体。这些方法 然后将应用于为sEH和TSPO确定新的长停留时间抑制剂，这两个系统 滞留时间已被证明对药物疗效很重要。