Development of data-driven methods for de novo design of novel enzymes.

开发用于新型酶从头设计的数据驱动方法。

• 批准号: 2890692
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890692
• 关键词: Development; data; driven; methods; de

De novo protein design is quickly becoming a viable strategy for creating novel protein structures, especially given recent advances in structure prediction using deep-learning based methods such as AlphaFold [1]. However, it remains highly challenging to add complex functionality to these molecules [2]. While there has been some success in designing novel enzymes, their enzymatic activity falls short of most natural systems, making it necessary to perform directed evolution to improve activity [3]. In nature, cofactors are often incorporated into proteins to add a vast array of functionality, and so they offer an attractive route to create highly-active enzymes.This project aims to develop novel methods for designing proteins that incorporate cofactors. These tools will build on existing technology developed in the Wood lab, that utilises structural analysis and machine learning to produce and evaluate novel-protein sequences [4,5], supported by the Jarvis Lab who are experts in the design of artificial enzymes [6]. Throughout the PhD, the student will develop data-driven methods to design and understand sequences that bind cofactors that have applications in photochemistry. If realised, these proteins will have a transformational impact in the field biocatalysis.This project is primarily computational, although there may be some opportunity to perform experiments in the lab. While advantageous, experience in programming/machine learning and/or statistics is not required. We are adept at training people in these areas and the student will be well supported. All that is required is enthusiasm and determination to learn these skills.The student that takes on this project will form part of a larger cross-institutional team with the Universities of Manchester and Bristol, and there will be opportunities to spend some time in these institutions during course of the PhD.

从头蛋白质设计正在迅速成为创建新蛋白质结构的可行策略，特别是考虑到使用基于深度学习的方法（如AlphaFold）进行结构预测的最新进展[1]。然而，向这些分子添加复杂的功能仍然具有高度挑战性[2]。虽然在设计新型酶方面取得了一些成功，但它们的酶活性福尔斯低于大多数天然系统，因此有必要进行定向进化以提高活性[3]。在自然界中，辅因子通常被掺入蛋白质中以增加大量功能，因此它们提供了一种有吸引力的方法来创建高活性酶。本项目旨在开发设计辅因子的蛋白质的新方法。这些工具将建立在Wood实验室开发的现有技术的基础上，该技术利用结构分析和机器学习来生产和评估新型蛋白质序列[4，5]，并得到Jarvis实验室的支持，Jarvis实验室是人工酶设计的专家[6]。在整个博士学位期间，学生将开发数据驱动的方法来设计和理解结合在光化学中应用的辅因子的序列。如果实现，这些蛋白质将在生物催化领域产生变革性的影响。该项目主要是计算，尽管可能有一些机会在实验室进行实验。虽然有利，但不需要编程/机器学习和/或统计学方面的经验。我们擅长培训这些领域的人才，学生将得到很好的支持。所有需要的是学习这些技能的热情和决心。承担这个项目的学生将与曼彻斯特大学和布里斯托大学组成一个更大的跨机构团队的一部分，并且在博士课程期间将有机会在这些机构度过一段时间。