Computational design of novel proteins to bind unnatural cofactors

广告项目描述（最多500字）。这将是向未来的学生宣传的文本：从头开始的蛋白质设计正迅速成为创造新型蛋白质结构的可行策略，尽管为这些结构添加功能仍然具有挑战性。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，因此有必要进行定向进化以提高活性[2]。在自然界中，辅因子通常被结合到蛋白质中以增加大量的功能，因此它们为创造高活性酶提供了一条有吸引力的途径。实验确定的酶结构有7万种，其中约有1.2万种含有辅助因子。这个数据集包含了大量的信息，可以用来设计结合新辅因子的蛋白质。这个项目的目的是应用Wood实验室正在开发的基于深度学习的工具来设计新的蛋白质，这些蛋白质可以结合小分子催化剂作为非天然辅因子。我们的目标分子是高活性的、生物相容性的催化剂，通过将它们与蛋白质结合，我们的目标是调整底物特异性，以及产物的对映体选择性。该设计策略利用深度学习来识别与天然辅因子在化学上相似的小分子区域，并利用结合辅因子的蛋白质的已知结构信息来指导设计过程。在利用爱丁堡基因组铸造厂提供的机器人设备进行大规模实验验证之前，将产生大量的设计并进行计算筛选。在进行详细的催化、生物物理和结构分析之前，将对设计进行辅因子结合筛选。在此过程中产生的信息将被合并到后续的设计中，从而创建一个工程“设计、构建、测试”周期。在整个项目中，学生将接受实验自动化、分子生物学、生物物理技术和结构表征方面的培训。此外，华莱士实验室将支持所设计酶的生化特性培训。虽然这个项目的主要重点是实验，但学生将有充分的机会发展机器学习和蛋白质原子模拟的计算技能。本课程不需要有计算技术方面的先验知识，因为学生将在开发这些技能方面得到很好的支持，但有Python编程语言的基本编程经验将是有利的。

Project Description for Advert (max 500 words). This will be the text that is advertised to prospective students:De novo protein design is quickly becoming a viable strategy for creating novel protein structures, although adding functionality to these structures remains challenging [1]. 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 [2]. In nature, cofactors are often incorporated into proteins to add a vast array of functionality, and so they offer an attractive route to creating highly active enzymes. There are > 70,000 experimentally determined structures of enzymes, and of these around 12,000 contain cofactors [3]. This dataset contains a huge amount of information that could be utilised to design proteins that bind novel cofactors.The aim of this project is to apply deep learning-based tools, which are being developed in the Wood lab, to design novel proteins that can bind small-molecule catalysts as unnatural cofactors. The molecules that we are targeting are highly active, biocompatible catalysts, and by binding them to a protein, we aim to tune substrate specificity, as well as enantioselectivity of the products. The design strategy deploys deep learning to identify regions of the small molecule that are chemically similar to natural cofactors, and exploits known structural information for proteins that bind cofactors to guide the design process. Large numbers of designs will be generated and screened computationally, before the most promising candidates are taken forward for experimental validation at scale, taking advantage of the robotics facilities available through the Edinburgh Genome Foundry. Designs will be screened for cofactor binding before a subset are taken forward for detailed catalytic, biophysical and structural analysis. Information generated during this process will be incorporated into subsequent rounds of design, creating an engineering "design, build, test" cycle.Throughout this project, the student will receive training in experimental automation, molecular biology, biophysical techniques, and structural characterisation. Furthermore, the Wallace lab will support training in biochemical characterisation of the designed enzymes. While the major focus of this project is experimental, there will be ample opportunity for the student to develop computational skills in machine learning and atomistic simulation of proteins. No prior knowledge of computational techniques is required, as the student will be well supported in developing these skills, but experience of basic programming with the Python programming language would be advantageous.