Computational and theoretical characterization of ligand-protein binding mechanis

配体-蛋白质结合机制的计算和理论表征

• 批准号: 9098765
• 负责人: Chia-en Chang
• 金额: $ 27.38万
• 依托单位: UNIVERSITY OF CALIFORNIA RIVERSIDE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9098765
• 关键词: Affinity; Automobile Driving; Behavior; Binding; Binding Proteins; Biology; CDK2 gene; Cell physiology; Cereals; Chemicals; Chemistry; Computer Simulation; Computing Methodologies; Data; Development; Disease; Dissociation; Drug Design; Entropy; Free Association; Free Energy; Functional disorder; Goals; Health; Kinetics; Knowledge; Life; Ligand Binding; Ligands; Link; MAP Kinase Gene; MAPK14 gene; Mediating; Medicine; Methods; Modeling; Molecular; Molecular Models; Motivation; Movement; Pathway interactions; Peptides; Pharmaceutical Preparations; Phase; Phosphopeptides; Play; Population; Process; Property; Protein Conformation; Protein Dynamics; Proteins; Research; Role; Safety; Solvents; Structure; Surface; System; Testing; Thermodynamics; Time; Water; Work; base; biological systems; computerized tools; cost; design; drug candidate; drug discovery; drug efficacy; flexibility; functional group; inhibitor/antagonist; innovation; molecular dynamics; molecular modeling; molecular recognition; novel; novel therapeutics; protein aminoacid sequence; protein function; protein protein interaction; research study; simulation; theories; tool

DESCRIPTION (provided by applicant): The goal of this proposal is to build a more complete picture of ligand-protein recognition by examining free, intermediate and bound states in order to reveal the why and when of binding mechanisms and kinetic behavior. Non-covalent molecular recognition plays a crucial role in biology, chemistry and medicine. Exploring binding pathways and the transient intermediate states during binding will help elucidate mechanisms that include binding, allostery, induced fit, gated control associations, and the free energetics o binding, which will later guide molecular designs. Molecular simulations play an increasing role in studying binding thermodynamics and kinetics, important in both biology and chemistry. Used in combination with experiments, simulations integrate data and interpret experiments; then contribute to the design of novel molecules with preferred affinities and/or kinetic properties. Kinetic data also can be used as a critical differentiator and predictor for drug efficacy and safety. However, real molecular systems are quite complicated, and computational tools usually are either very time-consuming or over-simplify a biological system. Therefore, the major motivation is that we need projects to further develop new computational methods to both efficiently and accurately model molecular association pathways in order to understand the binding mechanisms, solvent effects and kinetic behavior. Guided by strong preliminary results and existing methods developed by our group, three specific aims are proposed: 1) Develop and apply multi-scale methods to model ligand-protein binding in order to understand binding mechanisms and kinetic behavior; 2) Investigate the role of waters and free energy landscape in binding processes; 3) Adapt and apply the new methods and integrate experiments to peptide- protein binding to study protein function and assist peptide design. The approach is innovative that it involves bringing new methodological breakthroughs to enable realistic modeling of binding pathways and expanding the classical view of molecular recognition. The proposed research is significant, because it provides computational tools for us to study binding processes, free energy surfaces and solvent effects, and reveals fundamental mechanisms of molecular association.

描述（由申请人提供）：该提案的目标是通过检查游离、中间和结合状态来构建配体-蛋白质识别的更完整的图像，以揭示结合机制和动力学行为的原因和时间。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。探索结合途径和结合过程中的瞬态中间状态将有助于阐明机制，包括结合，变构，诱导适应，门控控制协会和结合的自由能，这将在以后指导分子设计。分子模拟在研究结合热力学和动力学方面发挥着越来越重要的作用，在生物学和化学方面都很重要。与实验结合使用，模拟集成数据并解释实验;然后有助于设计具有优选亲和力和/或动力学性质的新型分子。动力学数据也可以用作药物疗效和安全性的关键区分和预测因素。然而，真实的分子系统是相当复杂的，并且计算工具通常要么非常耗时，要么过度简化生物系统。因此，主要的动机是我们需要项目来进一步开发新的计算方法，以有效和准确地模拟分子缔合途径，以了解结合机制，溶剂效应和动力学行为。在前期研究成果和已有方法的指导下，本文提出了三个具体目标：1）发展和应用多尺度方法模拟配体-蛋白质结合，以了解结合机理和动力学行为; 2）研究沃茨和自由能景观在结合过程中的作用; 3）将新方法应用于肽-蛋白质结合实验，研究蛋白质功能，辅助肽的设计。该方法是创新的，它涉及到带来新的方法学突破，使现实的结合途径建模和扩大分子识别的经典观点。这一研究具有重要意义，因为它为我们研究结合过程、自由能表面和溶剂效应提供了计算工具，并揭示了分子缔合的基本机制。