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

可测序序列定义的大环低聚聚氨酯的结构和功能的出现

• 批准号: 2203354
• 负责人: Eric Anslyn
• 金额: $ 60万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203354&HistoricalAwards=false
• 关键词: Emergence; Structure; Function; Sequenceable; Sequence

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.

在化学催化（CAT）计划的支持下，以及化学系化学合成（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教授正在开发一种通用协议，用于优化非生物（非天然）聚合物的分子识别和催化。天然肽的化学结构，α-氨基酸的聚合物，控制它们作为材料，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肽的非天然类似物，Anslyn小组将通过自动化方法制备大环化合物（一种常见的连接），测试它们对神经毒剂替代物降解的催化活性，评估有希望命中的精细结构，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精细结构信息与所需的反应性联系起来，并建立基本的联系。这种精心制作的数据收集和分析系统被用于确定如何指导非天然超分子催化剂合成的多变量过程，以与用于V-试剂替代物的选择性水解的酶竞争并扩大酶的活性。该计划进一步用于将数据科学和自动合成概念整合到Anslyn教授运行的两个基于课程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创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，催化剂生产已经相当滞后。Anslyn教授和他的研究团队正致力于通过创建一种新的超分子催化剂优化方法来解决超分子化学中的这一空白。正在开发一种综合方案，该方案结合：1）计算机控制的从一组精选单体合成大环低聚氨基甲酸酯，2）分析这些折叠体型催化剂对神经毒剂替代物水解的活性，3）测序程序以阐明低聚氨基甲酸酯结构，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导致改进的结合和催化。基于ML预测，将使用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。 这些活动将用于培训Anslyn小组的研究生和本科生研究人员，并进一步支持本科生的课程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价值和更广泛的影响审查标准进行评估，被认为值得支持。