A step change in the modelling of enzymatic catalysis

酶催化建模的重大变化

• 批准号: 2449420
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2449420
• 关键词: step; change; modelling; enzymatic; catalysis

A deep understanding of enzymatic processes requires an atomic-level description, which is often beyond the reach of experimental techniques but can be achieved through synergy with computational chemistry. This purely computational project proposes a step change in the modelling of an enzyme's active site by building on the foundations laid by a novel force field method called FFLUX[1]. This method is based on the best modern definition of an atom inside a molecular system[2]. FFLUX uses machine learning to train these atoms to "know what to do" in a previously unseen atomic configuration. In other words, quantum mechanically accurate energies and forces between atoms in an enzyme's active site are readily available. Hence, the potential energy surface of the active site can be quickly and accurately explored, both statically and dynamically. A most suitable prototype case for using FFLUX in enzyme engineering is that of terpenoids. They are the most abundant and largest class (>75,000) of natural products[3]. Most are commonly found in plants, with biological roles ranging from interspecies communication to intracellular signalling and defence against predatory species. Their commercial use is wide ranging as pharmaceuticals, herbicides, flavourings, fragrances and biofuels. Molecular dynamics simulations suggest that the monoterpene synthase class of enzymes do not undergo large-scale conformational changes during the reaction cycle (after initial substrate binding), making them attractive targets for structured-based protein engineering to expand their catalytic scope toward alternative monoterpene scaffolds. This very important class of compounds has been thoroughly studied in the MIB, which enriches the scope for interaction with experimentalists. The aim of the project is to create a step change in the realism of modelling an enzyme's active site. A rigorous yet accessible quantum mechanical description of a reaction is vital for ultimate progress. Only with detailed atomic insight into the mechanism of an enzymatic reaction, using FFLUX-for-enzymes, can one correctly rationalise the design of future enzymes.

对酶过程的深入理解需要原子水平的描述，这通常超出了实验技术的范围，但可以通过与计算化学的协同作用来实现。这个纯粹的计算项目提出了一个步骤的变化，通过建立在一个新的力场方法，称为FFLUX[1]奠定的基础上的酶的活性位点的建模。这种方法基于分子系统中原子的最佳现代定义[2]。FFLUX使用机器学习来训练这些原子，使其在以前看不见的原子配置中“知道该做什么”。换句话说，酶活性位点中原子之间的量子力学精确能量和力是现成的。因此，活性位点的势能面可以被快速准确地静态和动态地探测。 在酶工程中使用FFLUX的最合适的原型案例是萜类化合物。它们是最丰富和最大的天然产品类别（> 75，000）[3]。大多数常见于植物中，其生物学作用包括种间通信、细胞内信号传递和防御捕食性物种。它们的商业用途广泛，如制药，除草剂，调味品，香料和生物燃料。分子动力学模拟表明，单萜合酶类的酶在反应周期中（初始底物结合后）不会发生大规模的构象变化，使其成为基于结构的蛋白质工程的有吸引力的目标，以扩大其催化范围，朝向替代单萜支架。这类非常重要的化合物已经在MIB中进行了彻底的研究，这丰富了与实验人员互动的范围。 该项目的目的是在模拟酶的活性位点的现实主义中创造一个步骤。对反应进行严格而又易于理解的量子力学描述对于最终的进展至关重要。只有对酶促反应的机制有了详细的原子洞察力，使用FFLUX-for-enzymes，才能正确合理地设计未来的酶。