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Computational tools for enzyme engineering: bridging the gap between enzymologists and expert simulation

酶工程计算工具：弥合酶学家和专家模拟之间的差距

基本信息

批准号：
BB/L018756/1
负责人：
Adrian Mulholland
金额：
$ 18.61万
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：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FL018756%2F1
关键词：
Computational tools enzyme engineering bridging

项目摘要

It is becoming increasingly popular to use the powerful principles present in nature to our advantage. A key example is the extraordinary ability of organisms to make molecules with high specificity (pure, potentially complex molecules are obtained) and efficiency (little energy is used). Nature uses enzymes, proteins that act as catalysts, to achieve this. These enzymes typically work under mild conditions. Enzymes are already used in industry to make molecules that we require in cost-efficient, comparatively green and sustainable processes. However, nature has not provided us with an enzyme to suit the production of every desired molecule; typically enzymes only catalyze specific chemical reactions with specific starting materials. But the process of evolution teaches us that enzymes may be malleable for engineering different properties. For example, making small changes (mutations) in specific amino acids (the building blocks of proteins) of enzymes can allow these enzymes to accept different substrates and thereby catalyze the formation of new, desired molecules. Even though it is possible to determine the positions of atoms in an enzyme with great detail (e.g. using X-ray crystallography), the full effects of making changes to amino acids are not evident. This limits researchers in assessing what the (beneficial or non-beneficial) effects of such mutations are. It is possible to predict these effects with sophisticated computer simulation methods, but performing the necessary simulations requires expert knowledge. The researchers that are involved in optimizing enzymes to obtain new catalysts for making desired molecules are therefore usually limited to guessing what the effect of mutations is based on static structures alone. To bridge the gap between such experimental researchers and those that are experts in computer simulation, we aim to make expert simulation methods available through an interface that is familiar to the experimental researchers. Our project will involve the development of simulation protocols that assess the effects of mutations, using state-of-the-art methods that include molecular dynamics simulations and quantum chemistry calculations. The protocols will be designed such that they can be run on standard computers and they will be made accessible through an easy and familiar interface for experimental researchers (without the need for in-depth training in computer simulation). In addition, the protocols will allow high-throughput screening of 100s of mutations on high-performance computer clusters. The end result will be that researchers not skilled in computer simulation can easily assess the potential influence of mutations using their own computers, and that high-throughput screening of enzyme variants can be performed computationally. This can potentially save a lot of time and resources in the process of adapting an enzyme for a desired reaction. In addition, collaboration between researchers with complementary skills will be encouraged. The tools developed will also be beneficial in related fields, for example in designing effective drugs and understanding inheritable diseases.

利用自然界中存在的强大原理为我们所用正变得越来越流行。一个关键的例子是生物体具有非凡的能力，可以制造高特异性（获得纯的，可能复杂的分子）和高效率（使用很少的能量）的分子。大自然使用酶，蛋白质作为催化剂，来实现这一目标。这些酶通常在温和的条件下工作。酶已经在工业中用于生产我们所需的分子，这些分子是成本效益高、相对绿色和可持续的过程。然而，大自然并没有为我们提供一种适合生产每一种所需分子的酶;通常酶只催化特定起始材料的特定化学反应。但进化的过程告诉我们，酶可能具有可塑性，可以设计不同的特性。例如，在酶的特定氨基酸（蛋白质的结构单元）中进行微小的改变（突变）可以使这些酶接受不同的底物，从而催化新的所需分子的形成。尽管可以非常详细地确定酶中原子的位置（例如使用X射线晶体学），但对氨基酸进行改变的全部影响并不明显。这限制了研究人员评估这些突变的（有益或无益）影响。可以用复杂的计算机模拟方法预测这些影响，但进行必要的模拟需要专业知识。因此，参与优化酶以获得用于制造所需分子的新催化剂的研究人员通常仅限于猜测突变的影响仅基于静态结构。为了弥合这些实验研究人员和计算机模拟专家之间的差距，我们的目标是通过实验研究人员熟悉的界面提供专家模拟方法。我们的项目将涉及开发评估突变影响的模拟协议，使用最先进的方法，包括分子动力学模拟和量子化学计算。这些协议的设计将使它们可以在标准计算机上运行，并且实验研究人员可以通过简单而熟悉的界面访问它们（无需深入的计算机模拟培训）。此外，该协议将允许在高性能计算机集群上高通量筛选100个突变。最终的结果是，不熟悉计算机模拟的研究人员可以使用自己的计算机轻松评估突变的潜在影响，并且可以通过计算进行酶变体的高通量筛选。这可以在使酶适应所需反应的过程中潜在地节省大量时间和资源。此外，将鼓励具有互补技能的研究人员之间的合作。开发的工具也将有益于相关领域，例如设计有效的药物和了解遗传性疾病。

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。