Revealing the Ligand Binding Landscape with Advanced Molecular Simulation Methods

项目摘要 人体是不断变化的生命系统。药物在以下范围内采取行动 这种非平衡背景：药物被摄入后，它被吸收，分布到组织中，结合（都是靶向的 和脱靶）、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， 药物的功效在很大程度上是这些速率的函数。相比之下，药物发现的主导范式 亲和力的优化，其本身不足以确定结合速率（kon）和未结合速率 （koff）。虽然结合亲和力是koff和kon的比率，但是较长的停留时间可以导致较高的结合亲和力。 结合亲和力，这些不是很好的相关性，因为kon值可以从扩散限制（109 M-1 s-1）到 <104 M-1 s-1，用于自由度较低的蛋白质靶点，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，仅取决于端点 结合动力学取决于配体结合中编码的分子细节。 过渡态-自由能的最高点沿着结合途径。分子动力学模拟 可以用来研究这些过渡态的原子细节，但只是最近-授权的进展， 硬件和新的模拟算法-它是否能够模拟无偏配体结合， 释放事件，这可以耦合到长时间尺度的蛋白质运动。因此，人们对这一点知之甚少。 给定蛋白质靶标的配体结合过渡态，以及它如何从配体到配体发生变化。 通过WExplore增强型采样方法（由PI开发），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（事件仅在约1000秒内发生一次） 而不使用偏置力，这是对现有技术的显著改进。重要的是这 将能够分析两种蛋白质药物靶点上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18 kDA（TSPO））。这将标志着第一次研究 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量。 此外，这项工作将建立一种将过渡态属性编码到筛选工具中的方法 这是第一次可以根据动力学以高通量的方式筛选配体。这些方法 然后将应用于确定sEH和TSPO的新的长停留时间抑制剂，这两个系统， 停留时间已经显示出对药物功效是重要的。

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.