Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

这项提议的首要目标是计算模拟生物分子结合， 通过反复的实验，充分理解分子识别和结合 机制等我们将应用隐藏的自由能垒来修饰抑制剂， 动力学和使用自由能景观，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，配体的动力学行为 结合/解结合主要由短暂的看不见的中间体控制，这是非常困难的 进行实验性观察。计算机模拟提供了另一种解决方案， 描述和理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。引导优秀的 根据上一个资助期间取得的成果，提出了三个具体目标：1） 开发和应用方法来理解分子识别的机制和过程 为药物设计提供全面的图片和应用; 2）：了解 结合/解结合自由能曲线从多个途径和调查的影响 识别过程中的沃茨和侧链突变; 3）适应和应用新方法， 配体结合特异性和动力学，以了解非位点激酶靶点。该方法是 创新在于其对动力学行为控制的关注， 自由能的档案，并在此基础上现实主义，扩大了经典的分子观 识别.这项研究是有意义的，因为它全面模型免费 能量分布，动力学行为，详细的水效应，以及可能赋予药物 阻力重大成果：新的计算工具，以现实地设计配体， 优选的结合动力学，理解溶剂和突变效应，解释药物选择性。

The overarching goal of this proposal is to computationally model biomolecular binding, iteratively informed by experiments, to fully understand molecular recognition and binding mechanisms. We will apply hidden free energy barriers to modify inhibitors for preferred binding kinetics and use the free energy landscape 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 of ligand binding/unbinding is mainly governed by transient unseen intermediates, which are 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.