Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

项目总结/摘要 总体目标-计算模型生物分子结合，迭代通知 通过实验，充分了解分子识别和结合机制， 修饰抑制剂以获得优选结合动力学的隐藏自由能势垒及其用途 结合/非结合自由能剖面，以了解沃茨的作用以及如何和为什么 远离配体结合位点的残基有助于突变效应和配体选择性。 非共价分子识别在生物学、化学和医学中起着至关重要的作用。 动力学结合速率常数与平衡常数一起影响结合的速度、效力， 和非共价药物的安全性，并告知他们的设计。在某些情况下，结合动力学是 药物体内疗效的主要决定因素。然而，动力学行为主要由 通过配体结合/解结合过程中短暂的看不见的中间体， 实验观察。计算机模拟提供了另一种解决方案， 了解实验上看不见的现象，并为药物设计提供信息。 真实的分子系统是复杂和灵活的，需要新的建模工具， 理论来计算配体结合/解结合自由能曲线。组合使用 实验，我们的新的建模方法整合数据，并解释实验作为一个 设计具有优选结合动力学/亲和力的分子的前体。 在上一个资助期内取得的优异成绩的指引下，三个具体目标是 建议：1）开发和应用方法，以了解机制和过程， 分子识别，为药物设计提供全面的图像和应用; 2）： 了解来自多个途径的结合/解结合自由能曲线，并研究 识别过程中沃茨和侧链突变的影响; 3）适应和应用新的 配体结合特异性和动力学的方法，以了解场外激酶的目标。的 方法是创新的，其重点是控制动力学行为，先进的方法， 真实地模拟自由能分布，并基于这种现实主义，扩展经典观点 分子识别。这项研究具有重要意义，因为它全面 模型自由能剖面，动力学行为，详细的水的影响，和突变， 赋予耐药性。重大成果：用于实际设计的新计算工具 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.