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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.

所有的生物--生命本身--都依赖于酶。酶是大的天然分子，可以使特定的生化反应快速发生，也就是说酶是天然催化剂。它们是非常好的催化剂，但我们至今还不明白是什么使它们成为如此好的天然化学家。我们需要知道酶中的化学反应是如何发生的，这是很难通过实验来完成的。研究酶及其催化的反应有很多原因：许多药物是酶抑制剂（它们阻止特定的酶工作），因此更好地了解酶将有助于新药的设计。更好地了解单个酶也应该有助于理解和预测遗传变异的影响，例如理解为什么有些人可能从某种特定药物中受益，或者可能有疾病的风险。酶也是非常好的环境友好型催化剂-了解它们的功能有助于设计和开发新的“绿色”催化剂，用于法医，合成，分析和生物技术应用。酶在新兴的纳米技术领域也显示出作为“分子机器”的巨大潜力。我们将开展一个合作项目，将实验生物化学与先进的计算机建模方法结合起来，详细分析一种非凡的酶是如何工作的。这种酶催化一种不寻常的反应，并用于工业应用，但它可以通过提高效率来改进，我们希望通过设计改变它来做到这一点。我们将通过建模预测酶变化（突变）的影响，并通过实验验证我们的预测。我们将开发和应用新的高级建模方法，能够准确处理这些大型复杂系统以及它们催化的化学反应。同时进行实验和建模将有助于通过测试预测来发展方法，也有助于解释生化结果和规划新的实验（例如设计改变的酶）。我们将集中在亚磷酸脱氢酶，一种酶，催化一个化学独特的反应，到目前为止，一直逃避详细的机制的理解。目前的计算机建模方法对于研究酶反应的某些方面是有用的-它们提供了制作酶如何工作的分子“电影”的独特潜力-但具有重要的局限性。例如，酶的大尺寸和对密集计算的需要意味着当前的计算通常限于近似的并且通常不可靠的计算方法。酶催化机制的可靠预测需要更精确的技术。我们将扩展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，并分析反应如何受到酶动力学的影响。这项工作将与实验研究合作进行。实验数据将是计算的基本输入。我们将进行预测并与相同酶的实验进行比较，以测试我们的理论方法，使用分子模型分析和解释实验数据，并测试有关酶反应机制的假设。这种合作将涉及我们实验室之间的方法，数据，想法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研究的研究人员来说应该非常有用。

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

New methods: general discussion.

新方法：一般性讨论。