Using cell free systems and machine learning to prototype engineered protein designs

使用无细胞系统和机器学习来构建工程蛋白质设计原型

• 批准号: 2505619
• 金额: --
• 依托单位: Imperial College London
• 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-2505619
• 关键词: Using; cell; free; systems; machine

Cell-free systems have recently been developed as a prototyping platform for synthetic biology designs. The ability to screen a large number of genetic designs prior to transformation into a living chassis speeds up the design cycle and provides initial validation for predicted functionality. In this project, a collaboration with the UK synthetic biology company LabGenius will aim to develop a machine learning platform for protein engineering applied to a number of target proteins including biotherapeutics. The overall aim is to establish a cell-free workflow to allow the rapid assessment of engineered protein designs in vitro. A major challenge will be to ensure that the target proteins can be produced at a quality, yield, price point and purity required for down-stream testing. Another major aim of the project is to generate large datasets on specific target proteins which can then be used to inform and test the application of machine learning (ML) algorithms. A major challenge for this aim will be to ensure that the experimental platform is sufficiently robust to produce consistently reproducible results. If the datasets produced are too noisy, it will be very difficult to effectively use ML. We plan to use a number of non-commercially sensitive target proteins including an off-patent single chain antibody and industrially relevant enzyme (with available 3D structural information) to explore the validity of applying cell free measurements to assess engineered protein designs that are derived by machine learning algorithms. The technologies required for the core components of the proposed platform are already in place. The Freemont lab has already developed a low volume 384 well plate cell free assay platform that utilises the Labcyte acoustic liquid handling robot which will provide the basis of the proposed project. The cell-free prototyping platform is located in the London Biofoundry which Prof Freemont is co-director (https://www.londonbiofoundry.org/). Dr Baldwin has developed platforms for automated DNA assembly that will be used to construct the libraries of genetic variants. By assessing the specific known genetic constructs assembled, ML approaches developed by LabGenius will be used to iterate the design cycles.

无细胞系统最近被开发为合成生物学设计的原型平台。在转化为活底盘之前筛选大量基因设计的能力加快了设计周期，并为预测的功能提供了初步验证。在该项目中，与英国合成生物学公司LabGenius的合作旨在开发一个用于蛋白质工程的机器学习平台，该平台适用于许多目标蛋白质，包括生物治疗药物。总体目标是建立一个无细胞的工作流程，以允许在体外快速评估工程蛋白质设计。一个主要的挑战将是确保目标蛋白质可以以下游测试所需的质量、产量、价格和纯度生产。该项目的另一个主要目标是生成关于特定目标蛋白质的大型数据集，然后可用于通知和测试机器学习（ML）算法的应用。实现这一目标的一个主要挑战是确保实验平台足够稳健，以产生一致的可重复结果。如果产生的数据集太嘈杂，那么很难有效地使用ML。我们计划使用一些非商业敏感的靶蛋白，包括非专利单链抗体和工业相关酶（具有可用的3D结构信息），以探索应用无细胞测量来评估由机器学习算法衍生的工程蛋白设计的有效性。拟议平台的核心组成部分所需的技术已经到位。Freemont实验室已经开发出一种低容量384孔板无细胞检测平台，该平台利用Labcyte声学液体处理机器人，这将为拟议项目提供基础。无细胞原型平台位于伦敦生物实验室，Freemont教授是该实验室的联合主任（https：//www.londonbiofoundry.org/）。鲍德温博士已经开发了自动DNA组装平台，用于构建遗传变异库。通过评估组装的特定已知遗传结构，LabGenius开发的ML方法将用于验证设计周期。