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）和量子组合的计算机模拟 机械/分子机械（QM/mm）方法能够描述结构和动力学 由酶催化的蛋白质和化学反应。准确和 计算高效的能量函数是必要的。但是，仍然存在挑战： QM方法的精度，QM子系统的电子密度与 MM子系统的经典力字段，以及从头算QM/MM的成本资本化 关于相关QM方法的准确性和可靠性。为了应对这些挑战，我们 已经开发了一系列关于反应路径优化和自由的QM/mm的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（QM/mm-NN）方法。该建议旨在进一步开发QM/MM方法论 及其应用于重要酶中氧化还原过程的研究以及构造 从头算力场与神经网络表示结合。 我们的长期目标是开发和建立准确的基于第一原理和密度 基于功能理论（DFT）的MD和QM/MM模拟作为与实验的平等合作伙伴 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。我们的目的如下：（1）我们旨在使QM/mm型号从头开始 更准确的QM/mm能量，用于QM描述以及静电和VDW相互作用 在QM和MM子系统之间。 （2）我们旨在开发合并的计算 探索降低潜在可变性的关键分子决定因素的模型 金属蛋白。我们将提供有关化学和氧化还原反应机制的详细见解 生物系统，特别是laccases。 （3）我们旨在发展准确 水的力场和用于生物应用中模拟的蛋白质超出 传统的力场形成和准确性的限制。 拟议的发展将利用量子的理论发展 电子理论，例如线性响应理论和准确的多电子方法 非共价相互作用，并利用数据科学的机器学习方法 系统模拟。拟议的工作将导致QM/mm的Ab Inli算 方法和力场，以及蛋白质的结构功能范式的见解 以及酶的重要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论。