Emergence of Structure and Function from Sequenceable Sequence-Defined Macrocyclic Oligourethanes

在化学催化(CAT)计划的支持下，以及化学合成(SYN)和大分子、超分子和纳米化学(MSN)计划的共同资助下，德克萨斯大学奥斯汀分校的Eric V.Anslyn教授正在开发一种用于分子识别和催化的优化非生物(非天然)聚合物的通用协议。天然多肽是α-氨基酸的聚合物，其化学结构控制着它们作为材料、催化实体和信息载体的化学功能。为了开发具有替代可调反应性的非天然多肽类似物，Anslyn团队将通过自动化方法制备大环氨酯(一种常见的连接)，测试它们对降解神经毒剂替代品的催化活性，评估有希望的HITS的精细结构，并使用递归策略进行优化。机器学习方法也将被整合到优化协议中，以将低聚氨基甲酸酯的精细结构信息与所需的反应性联系起来，并建立基本联系。这个精心设计的数据收集和分析系统正被用来确定如何指导非天然超分子催化剂合成的多变量过程，以竞争和扩大酶的活性，以选择性地水解V-试剂替代品。该计划进一步用于将数据科学和自动化综合概念整合到由Anslyn教授运营的两个以课程为基础的本科化学研究计划中，并支持高中教师开发教育项目的研究经验。虽然超分子化学领域已经在制造受体方面取得了相当大的成功，但由于多变量优化的挑战，催化剂的生产已经相当滞后。Anslyn教授和他的研究团队正在努力通过创造一种新的方法来优化超分子催化剂，以解决超分子化学中的这一差距。正在开发一种综合方案，它结合了：1)计算机控制的从一组精选单体合成大环低聚尿烷，2)分析这些折叠型催化剂用于神经毒剂替代物的水解的活性，3)排序例程以阐明低聚尿烷结构，以及4)使用机器学习技术的递归优化。涉及偏最小二乘回归(PLSR)的算法正在序列空间和CD光谱上进行训练，以预测哪些单体及其协同作用会改善结合和催化性能。根据ML预测，将使用一个脚本对合成器进行重新编程，允许通过合成、筛选和测序进行递归循环，以使用自动化工作流程优化催化。这些活动将用于在安斯林小组培训不同的研究生和本科生研究人员，并进一步支持本科生的基于课程的研究经验和高中教师的教育研究经验。该奖项反映了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。

With the support from the Chemical Catalysis (CAT) program and co-funding from the Chemical Synthesis (SYN) and Macromolecular, Supramolecular and Nanochemistry (MSN) programs in the Division of Chemistry, Professor Eric V. Anslyn of the University of Texas-Austin, is developing a general protocol for optimizing abiotic (non-natural) polymers for molecular recognition and catalysis. The chemical structure of natural peptides, polymers of alpha-amino acids, controls their chemical function as materials, catalytic entities, and information carriers. To develop non-natural analogues of peptides with alternative tunable reactivity, the Anslyn group will prepare macrocycles of urethanes (a common linkage) via automated methods, test their catalytic activity for the degradation of nerve agent surrogates, assess the fine structure of promising hits, and use a recursive strategy for optimization. Machine learning methods will also be integrated into the optimization protocol to link oligo-urethane fine structure information to desired reactivity and to establish fundamental connections. This carefully crafted data collection and analysis system is being used to determine how to guide the multivariable process for non-natural supramolecular catalyst synthesis to compete with and expand the activities of enzymes for the selective hydrolysis of V-agent surrogates. This program is further being used to integrate data science and automated synthesis concepts into two course-based undergraduate chemistry research programs run by Professor Anslyn, and to support research experiences for high school teachers to develop educational projects. While the field of supramolecular chemistry has had considerable success creating receptors, catalyst production has lagged considerably due to the challenges of multivariable optimization. Professor Anslyn and his research team are working toward addressing this gap in supramolecular chemistry by creating a new approach to supramolecular catalyst optimization. An integrated protocol is being developed that combines: 1) the computer-controlled synthesis of macrocyclic oligourethanes from a curated group of monomers, 2) analysis of the activity of these foldamer-type catalysts for the hydrolysis of nerve agent surrogates, 3) sequencing routines to elucidate oligourethane structure, and 4) recursive optimization using machine learning techniques. Algorithms involving partial least squares regression (PLSR) are being trained on sequence space and CD spectroscopy to predict which monomers, and their synergy, lead to improved binding and catalysis. Based upon the ML predictions, a script will be used to reprogram the synthesizer, allowing recursive cycling through synthesis, screening, and sequencing to optimize catalysis using an automated workflow. These activities will be used to train a diverse group of graduate and undergraduate researchers in the Anslyn group and further supporting course-based research experiences for undergraduates and educational research experiences for high-school teachers.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.