Combined experimental and computational investigations of a nucleophilic displacement reaction with a hydride leaving group

氢化物离去基团亲核置换反应的实验和计算相结合的研究

基本信息

批准号：
EP/G002843/1
负责人：
Adrian Mulholland
金额：
$ 35.87万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2009
资助国家：
英国
起止时间：
2009 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FG002843%2F1
关键词：
Combined experimental computational investigations nucleophilic

项目摘要

All of biology - life itself - depends on enzymes. Enzymes are large, natural molecules that allow specific biochemical reactions to take place quickly, that is to say enzymes are natural catalysts. They are very good catalysts, but as yet we do not understand what it is that makes them such good natural chemists. We need to know how chemical reactions happen in enzymes, something that is very difficult to do by experiments alone. There are many reasons for studying enzymes and the reactions they catalyse: many drugs are enzyme inhibitors (they stop specific enzymes from working), so better understanding of enzymes will help in the design of new drugs. Better understanding of individual enzymes should also help understand and predict the effects of genetic variation, for example in understanding why some people may benefit from a particular drug, or may be at risk from a disease. Enzymes are also very good and environmentally friendly catalysts - knowing how they function should help in the design and development of new 'green' catalysts for forensic, synthetic, analytical and biotechnological applications. Enzymes also show great promise as 'molecular machines' in the emerging field of nanotechnology. We will carry out a collaborative project bringing together experimental biochemistry with advanced computer modelling methods to analyse in detail how a remarkable enzyme works. This enzyme catalyses an unusual reaction, and is used in industrial applications, but it could be improved by making it more efficient, which we hope to do by designing changes to it. We will predict the effects of changes to the enzyme (mutations) by modelling, and test our predictions experimentally. We will develop and apply new high-level modelling methods, capable of dealing accurately with these large and complex systems, and the chemical reactions they catalyse. Carrying out experiments and modelling together will help develop the methods, by testing them predictions, and will also help in interpreting biochemical results and planning new experiments (e.g. designing altered enzymes). We will focus on phosphite dehydrogenase, an enzyme that catalyses a chemically unique reaction that so far has eluded detailed mechanistic understanding. Current computer modelling methods are useful for studying some aspects of enzyme reactions - they offer the unique potential of making molecular 'movies' of how enzymes work - but have important limitations. For example, large size of enzymes, and the need for intensive calculations, means that current calculations are typically limited to approximate and often unreliable computational methods. Reliable predictions of enzyme catalytic mechanisms require more accurate techniques. We will extend high-level methods, previously validated in studies of chemical reactions of small molecules, to study reactions in enzymes. We will develop new, hybrid methods that can describe the energies of breaking and forming chemical bonds well, and analyse how the reaction is affected by the dynamics of the enzyme. This work will be carried out in collaboration with experimental studies. The experimental data will be essential input for the calculations. We will make predictions and compare with experiments on the same enzyme to test our theoretical methods, use molecular models to analyse and interpret experimental data and test hypotheses about the enzyme reaction mechanism. This collaboration will involve the transfer and exchange of methods, data, ideas and researchers between our labs. The new methods we develop will be made widely available, and should be very useful to biologists, biochemists and other researchers working on biological catalysis.

生物本身的所有生物学 - 取决于酶。酶是大型天然分子，可以迅速发生特定的生化反应，也就是说，酶是天然催化剂。它们是非常好的催化剂，但是我们尚不理解是什么使它们如此出色的天然化学家。我们需要知道化学反应是如何在酶中发生的，这是仅通过实验而很难做到的。研究酶及其催化的反应有很多原因：许多药物是酶抑制剂（它们阻止特定的酶起作用），因此对酶的更好了解将有助于设计新药。更好地了解单个酶也应有助于理解和预测遗传变异的影响，例如，了解某些人可能会从特定药物中受益，或者可能受到疾病的危险。酶也是非常好的和环保的催化剂 - 知道它们的功能应为法医，合成，分析和生物技术应用的新“绿色”催化剂的设计和开发。酶在纳米技术的新兴领域也表现出巨大的希望。我们将进行一个协作项目，将实验性生物化学与先进的计算机建模方法汇总在一起，以详细分析非凡的酶如何工作。这种酶会催化异常的反应，并用于工业应用中，但是可以通过提高效率来改进它，我们希望通过设计更改来做到这一点。我们将通过建模来预测变化对酶（突变）的影响，并通过实验测试我们的预测。我们将开发并应用新的高级建模方法，能够准确处理这些大而复杂的系统以及它们催化的化学反应。进行实验并共同进行建模将通过测试预测来帮助开发这些方法，还将有助于解释生化结果并计划新实验（例如设计改变的酶）。我们将专注于磷酸盐脱氢酶，磷酸盐脱氢酶是一种化学上独特反应的酶，到目前为止，它已经避免了详细的机理理解。当前的计算机建模方法对于研究酶反应的某些方面很有用 - 它们提供了使酶如何工作的分子“电影”的独特潜力 - 但具有重要的局限性。例如，酶的大尺寸以及对密集计算的需求，意味着当前的计算通常仅限于近似且通常不可靠的计算方法。酶催化机制的可靠预测需要更准确的技术。我们将延长以前在小分子化学反应研究中验证的高级方法，以研究酶中的反应。我们将开发新的混合方法，可以很好地描述断裂和形成化学键的能量，并分析如何受酶动力学影响反应。这项工作将与实验研究合作进行。实验数据将是计算的必要输入。我们将做出预测并与相同酶的实验进行比较，以测试我们的理论方法，使用分子模型来分析和解释实验数据，并检验有关酶反应机制的假设。这项合作将涉及我们实验室之间方法，数据，思想和研究人员的转移和交流。我们开发的新方法将被广泛使用，对生物学家，生物化学家和其他从事生物催化的研究人员应该非常有用。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Entropy of Simulated Liquids Using Multiscale Cell Correlation.

DOI：
10.3390/e21080750
发表时间：
2019-07-31
期刊：
Entropy (Basel, Switzerland)
影响因子：
0
作者：
Ali HS;Higham J;Henchman RH
通讯作者：
Henchman RH

Relative Affinities of Protein-Cholesterol Interactions from Equilibrium Molecular Dynamics Simulations.

DOI：
10.1021/acs.jctc.1c00547
发表时间：
2021-10-12
期刊：
Journal of chemical theory and computation
影响因子：
5.5
作者：
Ansell TB;Curran L;Horrell MR;Pipatpolkai T;Letham SC;Song W;Siebold C;Stansfeld PJ;Sansom MSP;Corey RA
通讯作者：
Corey RA

Biomolecular Simulations in the Time of COVID19, and After.