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) 和组合量子进行计算机模拟 机械/分子机械（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。一个准确且 计算有效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 MM 子系统的经典力场，以及从头开始 QM/MM 方法资本化的成本 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 开发了一系列关于反应路径优化的从头开始 QM/MM 方法，并且免费 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。该提案旨在进一步开发从头开始的 QM/MM 方法 及其在重要酶氧化还原过程研究中的应用以及构建 从头算力场与神经网络表示相结合。 我们的长期目标是开发和建立准确的第一性原理和密度 基于泛函理论 (DFT) 的 MD 和 QM/MM 模拟作为实验的平等伙伴 研究酶和蛋白质，并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) 我们的目标是从头开始建立 QM/MM 模型 更准确的 QM/MM 能量，用于 QM 描述以及静电和 vdW 相互作用 QM 和 MM 子系统之间。 （2）我们的目标是开发一种组合计算 模型探索还原电位变异性的关键分子决定因素 金属蛋白。我们将详细介绍化学和氧化还原反应机制 生物系统，特别是漆酶。 （三）以发展精准化为目标 水和蛋白质的力场用于生物应用中的模拟，超越了 传统的力场形式和精度限制。 拟议的发展将利用量子理论的发展 电子理论，例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进行生物研究 系统模拟。拟议的工作将导致从头开始的 QM/MM 的重大进步 方法和力场，以及对蛋白质结构功能范式的见解 以及酶中重要的氧化还原过程和反应机制。此外，这也将导致 新药和酶抑制剂设计的方法学开发。