Semi-Automated Discovery of Synthetic Polymers with Protein Features

酶是具有无与伦比的复杂性的生物催化剂。在某些情况下，数千万年的进化塑造了它们的结构和功能。为了创造具有新功能的酶，需要一种可以取代进化的设计策略。该项目的目标是将人工智能、聚合物科学和机器人技术结合起来，加快合成酶的设计和开发。多肽是一连串的氨基酸，是酶的组成部分。聚合物和多肽的结合将创造出比单纯使用多肽产生酶更多种类的结构和化学成分来进行评估。为了培养精通这种酶设计方法的未来劳动力，该项目将为研究生和本科生创造一个跨学科和身临其境的环境。来自传统代表性不足群体的学生将通过外展和参加罗格斯大学的培训计划积极招募。酶是典型的球状蛋白质，具有活性口袋，能够以特殊的特异性催化化学反应。单链聚合物纳米颗粒(SCNPs)疏水坍塌在催化元件周围，产生合成酶模拟物。目前还没有一种方法可以生产出可以呈现蛋白质样结构的定制聚合物。该项目的目标是开发能够塌陷并围绕催化配体形成结构的SCNPs，以产生模仿谷胱甘肽过氧化物酶和碳酸氢酶的结构和功能的球形纳米材料。中心假设是，通过自动化的物理化学景观的主动机器学习将为设计具有酶活性的催化SCNPs提供一个有效的过程。该项目将实施一个迭代和闭环的设计-构建-测试-学习过程，以揭示编码SCNP中酶模拟行为的潜在结构-功能行为。将完成以下三个目标：1)用于SCNPs序列水平合成控制的程序自动化，2)通过在自动化平台上的主动学习进行机器学习模型训练，以及3)深入分析结构-功能关系，以揭示潜在的生物物理行为。由此产生的模型和数据将作为数字资源发布，以服务于合成酶/SCNP社区。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Enzymes are biological catalysts with unrivaled complexity. In some cases, tens of millions of years of evolution have shaped their structure and function. To create enzymes with new capabilities, a design strategy that can replace evolution is needed. The goal of this project is to combine artificial intelligence, polymer science, and robotics to accelerate the design and development of synthetic enzymes. Peptides are strings of amino acids, the building blocks of enzymes. Combining polymers and peptides will create a greater variety of structures and chemistries to be evaluated than would be possible simply using peptides to create enzymes. To prepare a future workforce proficient in this approach to enzyme design, this project will create an interdisciplinary and immersive environment for graduate and undergraduate students. Students from traditionally underrepresented groups will be actively recruited via outreach and participation in training programs at Rutgers University.Enzymes are typically globular proteins with an active pocket capable of catalyzing chemical reactions with exceptional specificity. Single-chain polymer nanoparticles (SCNPs) hydrophobically collapse around catalytic elements, creating synthetic enzyme mimics. There is no current method that yields bespoke polymers that can assume protein-like structures. The goal of this project is to develop SCNPs that collapse and structure around catalytic ligands to produce globular nanomaterials that mimic the structure and function of glutathione peroxidase and carbonic anhydrase. The central hypothesis is that active machine learning through physicochemical landscapes via automation will provide an efficient process for designing catalytic SCNPs with enzymatic activity. This project will implement an iterative and closed-loop Design-Build-Test-Learn process to reveal underlying structure-function behavior that encode enzyme mimetic behavior in SCNPs. The following three objectives will be completed: 1) program automation for sequence-level synthetic control of SCNPs, 2) machine learning model training through active learning on an automated platform, and 3) in-depth analysis of structure-function relationships to reveal underlying biophysical behavior. The resulting models and data will be published open source to serve the synthetic enzyme / SCNP community as a digital resource.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.