Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

项目摘要/摘要 最重要的目标--迭代地建立生物分子结合的计算模型 通过实验，为了充分了解分子识别和结合机制，应用 隐藏的自由能势垒，用于修饰抑制剂以获得首选结合动力学，并使用 结合/解绑自由能分布，以了解水的作用以及如何和为什么 远离配基结合部位的残基有助于突变效应和配基选择性。 非共价分子识别在生物、化学和医学中发挥着至关重要的作用。 动力学结合速率常数与平衡常数一起影响速度、效率、 和非共价药物的安全性，并为它们的设计提供参考。在某些情况下，结合动力学是 一种药物体内疗效的主要决定因素。然而，动力学行为主要受 通过配体结合/解离过程中的瞬时看不见的中间体，很难 以实验的方式观察。计算机模拟提供了另一种解决方案，两者都是为了描述 理解实验中看不见的现象，并为药物设计提供信息。 真实的分子系统复杂而灵活，需要新的建模工具和 计算配体结合/解离自由能分布的理论。用于与…连用 实验，我们的新建模方法集成了数据并将实验解释为 设计具有首选结合动力学/亲和力的分子的先驱。 在上一次筹资期间取得的优异成绩的指导下，三个具体目标是 建议：1)开发和应用方法来理解 为药物设计提供全面图景和应用的分子识别；2)： 了解多个途径的结合/解结合自由能分布，并研究 水和侧链突变在识别过程中的影响；3)适应和应用新的 方法用配体结合的特异性和动力学来了解异位激活素靶点。这个 方法的创新之处在于它专注于控制运动行为，先进的方法 真实地模拟自由能分布，并在此基础上对经典观点进行扩展 分子识别的能力。拟议的研究具有重要意义，因为它全面地 模拟自由能分布、动力学行为、详细的水效应和可能 产生抗药性。重大成果：用于现实设计的新计算工具 具有优先结合动力学的配体，了解溶剂和突变效应，解释药物 选择性。

Project Summary/Abstract The overarching goal – Computationally model biomolecular binding, iteratively informed by experiments, to fully understand molecular recognition and binding mechanisms, apply hidden free energy barriers to modify inhibitors for preferred binding kinetics, and use binding/unbinding free energy profiles to understand the role of waters and how and why residues far from ligand binding site can contribute to mutation effects and ligand selectivity. Non-covalent molecular recognition plays a crucial role in biology, chemistry and medicine. Kinetic binding rate constants, together with equilibrium constants, affect the speed, efficacy, and safety of non-covalent drugs and inform their design. In some cases, binding kinetics are the major determinant of a drug’s in vivo efficacy. However, kinetic behavior is mainly governed by transient unseen intermediates during ligand binding/unbinding processes, very difficult to observe experimentally. Computer simulations offer an alternative solution, both for describing and understanding experimentally unseen phenomena and to inform drug design. Real molecular systems are complicated and flexible and call for new modeling tools and theories to compute ligand binding/unbinding free energy profiles. Used in combination with experiments, our new modeling approach integrates data and interprets experiments as a precursor to designing molecules with preferred binding kinetics/affinities. Guided by excellent results obtained during the previous funding period, three Specific Aims are proposed: 1) Develop and apply methods to understand mechanisms and processes of molecular recognition that provide a comprehensive picture and applications for drug design; 2): Understand the binding/unbinding free energy profile from multiple pathways and investigate the effects of waters and sidechain mutations during recognition; 3) Adapt and apply the new methods to ligand binding specificity and kinetics to understand off-site kinase targets. The approach is innovative in its focus on control of kinetic behavior, advanced methods to realistically model free energy profiles and, based on this realism, expand on the classical view of molecular recognition. The proposed research is significant because it comprehensively models free energy profiles, kinetic behavior, detailed water effects, and mutations that may confer drug resistance. Significant outcomes: New computational tools to realistically design ligands with preferred binding kinetics, understand solvent and mutation effects, explain drug selectivity.