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则来利用我们的优势，它变得越来越流行。一个关键的例子是生物体使具有高特异性的分子（获得纯净的，潜在的复杂分子）和效率（使用很少的能量）的非凡能力。大自然使用酶，充当催化剂的蛋白质来实现这一目标。这些酶通常在轻度条件下起作用。酶已经在行业中用于制造我们需要成本效益，相对绿色和可持续的过程所需的分子。但是，大自然并未为我们提供适合每个所需分子的生产的酶。通常，酶仅用特定的起始材料催化特定的化学反应。但是，进化过程告诉我们，酶可能具有工程不同特性的延展性。例如，在特定氨基酸（蛋白质的组成块）中做出小变化（突变）可以使这些酶接受不同的底物，从而催化新的，所需的分子的形成。即使可以用非常细节（例如使用X射线晶体学）确定原子在酶中的位置，但对氨基酸进行更改的全部效果并不明显。这限制了研究人员评估此类突变的（有益或非益生）的影响。可以通过复杂的计算机模拟方法预测这些效果，但是执行必要的模拟需要专家知识。因此，参与优化酶以获取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效果仅基于静态结构。为了弥合此类实验研究人员与计算机模拟专家之间的差距，我们旨在通过实验研究人员熟悉的界面来提供专家仿真方法。我们的项目将使用包括分子动力学仿真和量子化学计算的最新方法，涉及评估突变效应的仿真方案的开发。这些协议的设计将使它们可以在标准计算机上运行，并且可以通过简单且熟悉的界面来访问实验研究人员（无需在计算机模拟中进行深入培训）。此外，协议将允许在高性能计算机簇上高通量筛选100 s突变。最终结果是，不熟练计算机模拟的研究人员可以轻松地使用自己的计算机评估突变的潜在影响，并且可以通过计算进行高通量筛选。这可以节省大量的时间和资源，以适应酶以产生所需的反应。此外，将鼓励研究人员之间的合作。开发的工具也将在相关领域中有益，例如设计有效的药物和理解可继承的疾病。

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。