Computer Simulations of Enzymes

酶的计算机模拟

• 批准号: 10246502
• 负责人: Weitao Yang
• 金额: $ 36.04万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246502
• 关键词: Address; Adopted; Biochemical; Biochemical Process; Biological; Biophysical Process; Chemicals; Chemistry; Collaborations; Complement; Computer Models; Computer Simulation; Copper; Data Analyses; Data Science; Development; Electrons; Electrostatics; Enzyme Inhibitor Drugs; Enzymes; Equilibrium; Free Energy; Goals; Investigation; Laboratories; Laccase; Lead; Life; Mechanics; Metalloproteins; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Mutation; Neural Network Simulation; Oxidation-Reduction; Performance; Pharmaceutical Preparations; Potential Energy; Process; Property; Protein Dynamics; Protein Engineering; Proteins; Protocols documentation; Reaction; Research; Roentgen Rays; Role; Scanning; Scheme; Series; Site; Structure; Structure-Activity Relationship; Variant; Water; Work; base; biological systems; chemical reaction; cost; density; design; electron density; experimental study; improved; inhibitor/antagonist; insight; intermolecular interaction; machine learning method; molecular dynamics; molecular mechanics; neural network; novel therapeutics; perturbation theory; quantum; response; simulation; theories; tool

Computer simulations using molecular dynamics (MD) and the combined quantum mechanical/molecular mechanical (QM/MM) approach are capable of describing structures and dynamics of proteins and chemical reactions catalyzed by enzymes. An accurate and computationally efficient energy function is necessary. However, challenges remain: the accuracy of QM method, the compatibility between the electron density of the QM subsystem and classical force fields for the MM subsystem, and the cost of ab initio QM/MM methods capitalizing on the accuracy and reliability of the associated QM approaches. To address these challenges, we have developed a series of ab initio QM/MM approaches on reaction path optimizations and free energy calculations, the QM/MM minimum free-energy path (QM/MM-MFEP) and the QM/MM neural network (QM/MM-NN) methods. This proposal aims to develop further the ab initio QM/MM methodology and its applications to the studies of redox processes in important enzymes, and the construction of ab initio force fields combined with neural network representations. Our long-term goals are to develop and establish accurate first-principles based and density functional theory (DFT) based MD and QM/MM simulation as an equal partner with experiments for the study of enzymes and proteins and to provide insight into chemical and redox processes in biological systems. Our aims are as follows: (1) We aim to make ab initio QM/MM models for much more accurate QM/MM energies, for the QM description and for the electrostatic and vdW interactions between the QM and MM subsystems. (2) We aim to develop a combined computational model to explore the key molecular determinants of the reduction potential variability in metalloproteins. We will provide detailed insight into chemical and redox reaction mechanisms in biological systems, in particular laccases. (3) We aim at the development of accurate force fields of water, and proteins for simulations in biological applications, going beyond the traditional force field forms and limitation in accuracy. The proposed developments will capitalize on the theoretical developments in quantum electronic theory, such as the linear response theory and accurate many-electron approach for non-covalent interactions, and leverage machine-learning methods in data science for biological system simulations. The proposed work will lead to the major advancement of the ab initio QM/MM method and force fields, and insights into the structure-function paradigm for proteins and important redox process and reaction mechanisms in enzymes. In addition, it will also lead to methodology development for design of new drugs and enzyme inhibitors.

使用分子动力学（MD）和组合量子 力学/分子力学（QM/MM）方法能够描述结构和动力学 蛋白质和酶催化的化学反应。准确和 计算效率高的能量函数是必要的。然而，挑战依然存在： QM方法的准确性，QM子系统的电子密度与 经典力场的MM子系统，和成本的从头算QM/MM方法资本化 相关质量管理方法的准确性和可靠性。为了应对这些挑战，我们 发展了一系列关于反应路径优化的从头算QM/MM方法， 能量计算，QM/MM最小自由能路径（QM/MM-MFEP）和QM/MM神经网络 网络（QM/MM-NN）方法。该建议旨在进一步发展从头算QM/MM方法 及其在重要酶的氧化还原过程研究中的应用， 结合神经网络表示的从头算力场。 我们的长期目标是开发和建立精确的基于第一性原理和密度的 基于泛函理论（DFT）的MD和QM/MM模拟作为平等的合作伙伴， 研究酶和蛋白质，并提供深入了解化学和氧化还原过程， 生物系统。我们的目标如下：（1）我们的目标是建立一个从头算QM/MM模型， 更准确的QM/MM能量，用于QM描述和静电和vdW相互作用 在QM和MM子系统之间。(2)我们的目标是开发一个组合的计算 模型来探索还原潜力变异性的关键分子决定因素 金属蛋白我们将提供详细的洞察化学和氧化还原反应机制， 生物系统，特别是漆酶。(3)我们的目标是发展准确的 力场的水，和蛋白质的模拟在生物应用中，超越了 传统的力场形式和精度的局限性。 拟议的发展将利用量子力学的理论发展， 电子理论，如线性响应理论和精确的多电子方法， 非共价相互作用，并利用数据科学中的机器学习方法， 系统模拟所提出的工作将导致从头算QM/MM的重大进展 方法和力场，并深入了解蛋白质的结构-功能范式 以及酶中重要的氧化还原过程和反应机理。此外，还将导致 用于设计新药和酶抑制剂的方法学开发。