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

从头蛋白质设计正迅速成为创造新蛋白质结构的可行策略，特别是考虑到最近使用基于深度学习的方法(如AlphaFold)进行结构预测的进展[1]。然而，给这些分子添加复杂的功能仍然是极具挑战性的[2]。虽然在设计新型酶方面取得了一些成功，但它们的酶活性低于大多数自然系统，这使得有必要进行定向进化来提高活性[3]。在自然界中，辅因子经常被加入到蛋白质中以增加大量的功能，因此它们提供了一条有吸引力的途径来创造高活性的酶。这个项目旨在开发新的方法来设计包含辅因子的蛋白质。这些工具将建立在Wood实验室开发的现有技术之上，该技术利用结构分析和机器学习来产生和评估新型蛋白质序列[4，5]，并得到人工酶设计专家贾维斯实验室的支持[6]。在整个博士课程中，学生将开发数据驱动的方法来设计和理解结合辅助因子的序列，这些辅助因子在光化学中有应用。如果实现，这些蛋白质将在现场生物催化中产生转化影响。这个项目主要是计算的，尽管可能有一些机会在实验室进行实验。虽然有优势，但不需要编程/机器学习和/或统计方面的经验。我们擅长在这些领域培训人才，学生将得到很好的支持。所需要的是学习这些技能的热情和决心。接受这个项目的学生将成为曼彻斯特大学和布里斯托尔大学更大的跨机构团队的一部分，在博士课程期间将有机会在这些机构花费一些时间。

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.