Semi-automated discovery of synthetic polymers with protein features

细胞处理复杂功能的能力源于蛋白质的灵活性，蛋白质以高度动态的方式组装成有组织的结构并执行各种功能。研究人员将试图发现新的设计规则，以利用合成聚合物重现蛋白质的活性。机器学习将与自动化聚合物合成技术相结合，以实现这一目标。这种方法将为合成细胞提供必要的构建模块，同时揭示生命起源中尚未解决的问题。该项目还将吸引附近一所少数族裔服务机构的高中生和学生参与，提供研究和学习机会，鼓励学生从事STEM职业。利用工程技术和材料，蛋白质应该是可复制的。在实践中，目前还不可能如此精确地控制合成聚合物的三维结构。这样做将使合成大分子的从头设计成为可能，这些大分子可用于合成细胞和细胞成分的自下而上组装。这项工作的目标是发现具有蛋白质样特征的合成聚合物。设计标准将通过实现智能和数据驱动的设计-构建-测试-学习实验周期来确定。我们将1)开发用于数据处理和强化机器学习的定量模型，2)为聚合物dna架构实现智能的“设计-构建-测试-学习”例程，以及3)构建可编程组件。直到最近，这些目标还是不可能实现的。没有可靠的方法来制作大量定义良好的聚合物库。最近发现的耐氧和自动化活性聚合物化学使得可靠和常规地设计-构建具有类似蛋白质特征的复杂聚合物结构成为可能。在这个项目中，自动化将升级为高通量分析和机器学习，以建立定量的结构-活动关系模型，用于紧凑和有组织结构的从头设计。这种对大分子组装高度多样化景观的半自动和强化探索将导致类似蛋白质的聚合物的设计，并将其用作合成细胞和细胞成分组装的分子构建块。该项目由英国理工学院/CBET的细胞和生化工程项目以及MPS/CHE的大分子、超分子和纳米化学项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ability of cells to care out complex functions arises from the flexibility of proteins, which assemble into organized structures and carry out various functions in a highly dynamic way. The investigators will attempt to discover new design rules to reproduce the activity of proteins using synthetic polymers. Machine learning will be coupled to automated polymer synthesis techniques to accomplish this goal. This approach will provide the necessary building blocks for synthetic cells while uncovering unsolved questions in the origins of life. The project will also engage high school and students at a nearby minority-serving institution, providing research and learning opportunities that will encourage students to pursue STEM careers.Proteins should be reproducible using engineering techniques and materials. In practice, it is not yet possible to control the 3-D structure of synthetic polymers with such precision. Doing so would enable the de novo design of synthetic macromolecules that can be used for the bottom-up assembly of synthetic cells and cell components. The goal of this work is to discover synthetic polymers with protein-like features. Design criteria will be identified by implementing intelligent and data-driven Design-Build-Test-Learn cycles of experimentation. We will 1) develop quantitative models for data handling and reinforced machine learning, 2) implement intelligent ‘Design-Build-Test-Learn’ routines for polymer-DNA architectures, and 3) build programmable assemblies. Until recently, these objectives were not possible. Robust methods for making large libraries of well-defined polymers were not available. The recent discovery of oxygen tolerant and automated living polymer chemistry makes it possible to reliably and routinely Design-Build complex polymer architectures with features similar to proteins. In this project, the automation will be upgraded with high throughput analytics and machine learning to establish quantitative structure-activity relationship models for the de novo design of compact and organized structures. Such semi-automatic and reinforced exploration of the highly diverse landscape of macromolecular assembly will lead to the design of protein-like polymers and their use as molecular building blocks for the assembly of synthetic cells and cell components.This project is being funding jointly between the Cellular and Biochemical Engineering Program in ENG/CBET and the Macromolecular, Supramolecular, and Nanochemistry Program in MPS/CHE.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.