Determination of structure, dynamics and energetics of enzyme reactions

酶反应的结构、动力学和能量学测定

• 批准号: 10456219
• 负责人: OLAF G WIEST
• 金额: $ 32.15万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456219
• 关键词: Active Sites; Anti-Bacterial Agents; Biochemistry; Biological; Biology; Biophysics; Catalysis; Chemicals; Cholesterol; Code; Collaborations; Combined Modality Therapy; Communities; Complex; Computational Biology; Computing Methodologies; Couples; Coupling; Crystallization; Crystallography; Data; Developed Countries; Development; Disease; Drug Targeting; Enzymatic Biochemistry; Enzymes; Equilibrium; Free Energy; Freezing; Goals; Grant; Health; Human; Hydroxymethylglutaryl-CoA reductase; Knowledge; Life; Link; Machine Learning; Maps; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Movement; Mutagenesis; Nature; Oxidoreductase; Pathway interactions; Pharmaceutical Preparations; Positioning Attribute; Process; Protein Dynamics; Proteins; Pseudomonas; Public Health; Reaction; Research Personnel; Resolution; Roentgen Rays; Role; Running; Science; Structure; System; Techniques; Time; Validation; Work; base; biophysical chemistry; cofactor; computer studies; design; electron density; enzyme mechanism; experience; experimental study; improved; innovation; machine learning method; millisecond; molecular dynamics; molecular mechanics; molecular scale; movie; new therapeutic target; particle; protein structure; quantum; simulation; structural biology; synthetic biology; theories; tool

Project Summary Understanding enzyme mechanisms is of paramount importance from both the basic biophysics perspective of understanding life processes and the role of enzymes in diseases. To achieve a detailed understanding of enzyme catalysis, the effects of protein structure and dynamics on the reaction energetics need to be elucidated. We propose a combined computational and experimental approach that combines the synthetic, computational and structural biology expertise of a team of investigators that has been working together for >15 years to create a “molecular movie” where the position, movement and energy of every atom in the system followed over the entire reaction pathway. The proposal exploits the emerging convergence of timescales accessible by molecular simulation using GPUs and time resolved structural biology. Specific Aim 1 describes the simulation of the complete reaction pathway of Pseudomonas mevalonii (Pm) HMGCoA Reductase (HMGR) and will use transition state force fields (TSFFs) generated by the quantum guided molecular mechanics method to allow the µsec MD simulations of the chemical steps. TSFFs not only circumvent the well-known boundary problem of QM/MM, but are also 102-104 times faster. This allows a realistic modeling of the coupling of µsec dynamics and catalysis that was demonstrated in the last grant period to be essential for understanding the reaction. Together with accelerated MD simulations of the conformational changes involved in the reaction using standard force fields, these computational studies cover the fsec to µsec timescale. In Specific Aim 2, the computational results will be merged with the results of a three-tiered approach to obtain structural snapshots with progressively increasing time resolution: (i) “Frozen” intermediates that map out the overall pathway on long timescales, (ii) time resolved Laue crystallography using pH jump initiation on the msec timescale and (iii) use of photocaged substrates to allow time resolved Laue experiments on the µsec timescale. This approach will be applied to the study of HMGR, an enzyme of high biophysical and biomedical significance that has a complex reaction mechanism involving three chemical steps, six large-scale conformational changes and two cofactor exchange steps. The project is highly innovative because it (i) uses a combination of MD simulations using TSFFs and time resolved crystallography to span timescales of at least 12 orders of magnitude, (ii) iteratively couples the Markov State analysis of long timescale trajectories to the Singular Value Decomposition used to analyze time resolved crystallography data, thus providing new tools to generate and experimentally validate trial structures (iii) applies global optimization and machine learning techniques to allow the automated fitting of TSFFs for proteins, which will enhance the application of this powerful method to other proteins and (iv) provides new photocaged substrates for the study of enzyme mechanisms to the chemical biology community. All tool compounds, methods and codes developed in this project will be made available to the scientific community.

项目总结