FET: Small: Tools and Experimental Validation for Predicting Enzymatic Promiscuity and its Products

通过合成生物学对微生物细胞进行工程改造，有望推动生物聚合物、燃料、营养品、治疗剂和其他特种产品等大宗商品化学品的生产。增加对微生物生理学基础上的代谢和调控网络的理解取决于开发捕获酶细胞活性的代谢模型。尽管测序技术和模型重建取得了重大进展，但仍有许多细胞酶活性尚不清楚。这个项目的假设是，未记载的酶的混杂活性导致了意想不到的反应副产物的形成。合成生物学家经常观察到这种现象，有时在设计过程中通过特别的实验努力加以利用。然而，目前预测、分析或减轻酶乱交影响的方法有限。该项目开发的工具，以预测化学计量平衡反应，反映未记录的混杂酶细胞活动。这些反应可以用来增强现有的代谢模型和改进设计工具。此外，该项目通过实验验证了增强模型，通过工程微生物宿主合成所需的化学物质，然后分析宿主与途径的相互作用。这项提议的工作是计算机科学和生物学的交叉，将提高预测和评估酶乱交对生物系统影响的能力。为研究生和本科生提供培训，并将研究纳入课堂教学。在计算机领域未被充分代表的少数族裔将通过各种已建立的国家和地方项目被招募参与研究。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 engineering of microbial cells through synthetic biology promises to advance the production of high-volume commodity chemicals such as biopolymers, fuels, nutraceuticals, therapeutics, and other specialty products. Increasing understanding of metabolic and regulatory networks underlying microbial physiology hinges on developing metabolic models that capture enzymatic cellular activity. Despite significant progress in sequencing technology and model reconstruction, there are many cellular enzymatic activities that remain unknown. The hypothesis underlying this project is that undocumented promiscuous activity of enzymes results in the formation of unexpected reaction byproducts. This phenomenon is frequently observed by synthetic biologists, and sometimes exploited during design through ad hoc experimental efforts. However, there are currently limited ways to predict, analyze, or mitigate the effects of enzyme promiscuity. This project develops tools to predict stoichiometrically balanced reactions that reflect undocumented promiscuous enzymatic cellular activities. These reactions can be utilized to augment existing metabolic models and improve design tools. Further, the project experimentally validates the augmented models by engineering a microbial host to synthesize desirable chemicals and then analyzing host-pathway interactions. The proposed work is at the intersection of computer science and biology and will advance the ability to predict and assess the impact of enzyme promiscuity on biological systems. Training will be provided to graduate and undergraduate students and the research will be incorporated into classroom teaching. Underrepresented minorities in computing will be recruited to participate in the research through various established national and local programs.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.