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Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

配体-蛋白质结合机制的计算和理论表征

基本信息

批准号：
10811524
负责人：
Chia-en Chang
金额：
$ 8.03万
依托单位：
UNIVERSITY OF CALIFORNIA RIVERSIDE
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-30 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10811524
关键词：
Acceleration Address Affect Affinity Animal Model Back Behavior Binding Binding Proteins Binding Sites Biological Assay Biology Chemicals Chemistry Computer Models Computer Simulation Computing Methodologies Data Dissociation Distal Drug Design Drug Targeting Drug resistance Environment Equilibrium Free Energy Funding Goals HIV Protease Infection Kinetics Knowledge Life Ligand Binding Ligands Link Medicine Methodology Methods Modeling Modification Molecular Molecular Conformation Mutate Mutation Outcome Oxidoreductase Pathway interactions Pharmaceutical Preparations Pharmacologic Actions Phosphotransferases Play Process Property Protein Kinase Proteins Research Research Personnel Role Safety Site Solvents Specificity Speed System Testing Thermodynamics Time Tularemia Water Work beta-Cyclodextrins clinically relevant computerized tools data integration design drug development drug-like compound experimental study flexibility glycogen synthase kinase 3 beta in vivo inhibitor innovation insight kinase inhibitor method development molecular recognition novel novel strategies off-target site receptor simulation theories tool

项目摘要

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.

这一提议的首要目标是对生物分子结合进行计算建模，通过实验反复了解，以充分了解分子识别和结合机制。我们将应用隐藏的自由能垒来修饰抑制剂以实现首选结合动力学和使用自由能图景来理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，配体的动力学行为绑定/解绑主要由暂时的看不见的中间体控制，这是非常困难的通过实验观察。计算机模拟提供了另一种解决方案，这两种方法都是描述和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。由卓越公司指导在上一次资助期间取得的成果，提出了三个具体目标：1) 发展和应用理解分子识别机制和过程的方法为药物设计提供全面的图景和应用；2)：了解结合/解绑多个途径的自由能分布，并研究 Waters和SideChain在识别过程中的突变；3)将新方法应用于配体结合的特异性和动力学，以了解异位激活酶靶标。方法是其创新之处在于注重对运动行为的控制，先进的建模方法逼真自由能分布，并在此现实主义的基础上，对分子的经典观点进行扩展承认。这项拟议的研究意义重大，因为它全面地模拟了能量分布、动力学行为、详细的水效应和可能提供药物的突变抵抗。重大成果：新的计算工具可用于现实地设计配体更好的结合动力学，了解溶剂和突变效应，解释药物选择性。

项目成果

期刊论文数量（33）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery.

DOI：
10.1007/s10822-018-0120-3
发表时间：
2018-06
期刊：
Journal of computer-aided molecular design
影响因子：
3.5
作者：
Cholko T;Chen W;Tang Z;Chang CA
通讯作者：
Chang CA

GeomBD3: Brownian Dynamics Simulation Software for Biological and Engineered Systems.

DOI：
10.1021/acs.jcim.1c01387
发表时间：
2022-05-23
期刊：
JOURNAL OF CHEMICAL INFORMATION AND MODELING
影响因子：
5.6
作者：
Cholko, Timothy;Kaushik, Shivansh;Wu, Kingsley Y.;Montes, Ruben;Chang, Chia-En A.
通讯作者：
Chang, Chia-En A.

Binding Kinetics Toolkit for Analyzing Transient Molecular Conformations and Computing Free Energy Landscapes.

DOI：
10.1021/acs.jpca.2c05499
发表时间：
2022-11-24
期刊：
JOURNAL OF PHYSICAL CHEMISTRY A
影响因子：
2.9
作者：
Ruzmetov, Talant;Montes, Ruben;Sun, Jianan;Chen, Si-Han;Tang, Zhiye;Chang, Chia-en A.
通讯作者：
Chang, Chia-en A.

Modeling Effects of Surface Properties and Probe Density for Nanoscale Biosensor Design: A Case Study of DNA Hybridization near Surfaces.

纳米级生物传感器设计的表面特性和探针密度的建模效应：表面附近 DNA 杂交的案例研究。