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Computational biochemistry: predictive modelling for biology and medicine

计算生物化学：生物学和医学的预测模型

基本信息

批准号：
EP/G007705/1
负责人：
Adrian Mulholland
金额：
$ 144.88万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FG007705%2F1
关键词：
Computational biochemistry predictive modelling biology

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. As yet we do not understand what it is that makes them such good natural catalysts. 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. It 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 industrial applications. Enzymes also show great promise as 'molecular machines' in the emerging field of nanotechnology. We will develop and apply advanced computer modelling methods , in collaboration with experimental biochemistry, to analyse in detail how enzymes work. We will study enzymes that are targets for designing drugs for the treatment of pain and anxiety, and study how drugs are broken down by enzymes in the body. We will develop new modelling methods, capable of dealing accurately with these large and complex systems, and the chemical reactions they catalyse. We will bring together state-of-the-art computer software and hardware, and new theoretical methods, to achieve unprecedented accuracy for modelling enzymes. These modelling methods promise to add an extra dimension to studying enzyme reactions - e.g. making molecular 'movies' of how enzymes work. We will also use the methods we develop to predict how strongly potential drugs bind to their protein targets. The methods we will develop and use are based on fundamental quantum mechanics, so will be better than current approximate techniques. Current methods for predicting how strongly different drugs bind to proteins are efficient, but lack reliability because they fail to capture the essential physics. Quantum mechanics provides a physically accurate representation of the interactions, but until now these methods have been too computationally intensive for practical use. We will base our developments on methods that can accurately model chemical reactions of small molecules, combined with techniques for modelling protein structure and dynamics, and extend these to study enzymes and their reactions. We will make use of the great power provided by the latest 'multi-core' computer chips. Altogether, this will require several ground-breaking developments, which we are well placed to carry out. We will develop and apply new methods that can calculate how reactions happen in enzymes, describing the energies of breaking and forming chemical bonds accurately and analyse how reaction is affected by protein dynamics. This work will be carried out in collaboration with experiments, with project partners in academia and industry, in the UK and abroad. We will make predictions and compare with experiments on the same enzymes to test our theoretical methods. This will involve the transfer and exchange of methods, data, ideas and researchers between experimental and modelling groups, in new and existing collaborations. The methods we develop and the results we obtain will be made widely available (e.g. via the web), and should be very useful to biologists, biochemists, drug designers and other researchers working on enzymes. We will extend these high-level methods to new areas of biology, to provide new tools for studying protein structure. The results should provide new and exciting insight into how enzymes function, and promise to make a major contribution to the development of new drugs.

所有的生物--生命本身--都依赖于酶。酶是一种大的天然分子，可以使特定的生化反应快速发生。到目前为止，我们还不明白是什么使它们成为如此好的天然催化剂。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。它还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获益，或者可能面临疾病的风险。酶也是非常好的环保催化剂-了解它们的功能有助于设计和开发工业应用的新型“绿色”催化剂。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。我们将开发和应用先进的计算机建模方法，与实验生物化学合作，详细分析酶的工作原理。我们将研究作为设计用于治疗疼痛和焦虑的药物的目标的酶，并研究药物如何被体内的酶分解。我们将开发新的建模方法，能够准确地处理这些大型复杂的系统，以及它们催化的化学反应。我们将汇集最先进的计算机软件和硬件，以及新的理论方法，以实现酶建模的前所未有的准确性。这些建模方法有望为研究酶反应增加额外的维度-例如制作酶如何工作的分子“电影”。我们还将使用我们开发的方法来预测潜在药物与其蛋白质靶点结合的强度。我们将开发和使用的方法基于基本量子力学，因此将优于当前的近似技术。目前预测不同药物与蛋白质结合强度的方法是有效的，但缺乏可靠性，因为它们未能捕捉到基本的物理学原理。量子力学提供了相互作用的物理精确表示，但到目前为止，这些方法的计算量太大，无法实际使用。我们将基于我们的发展方法，可以准确地模拟小分子的化学反应，结合蛋白质结构和动力学建模技术，并将其扩展到研究酶及其反应。我们将利用最新的“多核”计算机芯片所提供的强大功能。总而言之，这将需要几项突破性的发展，而我们已经做好了充分的准备。我们将开发和应用新的方法，可以计算反应如何在酶中发生，准确地描述断裂和形成化学键的能量，并分析反应如何受到蛋白质动力学的影响。这项工作将与实验合作进行，与学术界和工业界的项目合作伙伴，在英国和国外。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。这将涉及在新的和现有的合作中，在实验和建模小组之间转让和交流方法、数据、想法和研究人员。我们开发的方法和我们获得的结果将被广泛使用（例如通过网络），并且应该对生物学家，生物化学家，药物设计师和其他从事酶研究的研究人员非常有用。我们将把这些高级方法扩展到生物学的新领域，为研究蛋白质结构提供新的工具。这些结果将为酶的功能提供新的、令人兴奋的见解，并有望为新药的开发做出重大贡献。

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。