Collaborative Research: Directed Enzyme Evolution Accelerated by Machine Learning for Enhancing the Biodegradation of Emerging Contaminants

化学系的环境化学科学项目支持新泽西理工学院的李梦燕教授和埃德加多·法里纳斯教授以及亚利桑那州立大学的郑文伟教授进行这个项目。越来越多的人造化学品被排放到环境中，它们可能会恶化水质。生活在自然界中的细菌可以分泌酶来降解其中的一些化学物质，并将它们作为碳源加以利用。然而，自然发生的生物降解过程可能很慢或不足以去除这些污染物。在这个项目中，将在实验室对细菌酶进行修饰和优化，以加快其降解速度。实验结果将作为输入数据，用于训练模拟酶和污染物之间相互作用的计算模型。作为回报，这个计算模型将促进具有更大降解率的新酶的设计。该项目将发起积极的活动，以吸引研究生和本科生，特别是那些代表人数不足的群体的成员。将举办暑期交流讲习班，以促进新泽西理工学院和亚利桑那州立大学的学生和研究人员之间的交流。将通过结合基于实验室的酶评估和基于计算的机器学习来推广水污染物的绿色处理和技术创新。一些新出现的污染物因其在环境中的频繁检测和持久存在而对公众健康和自然生物群构成迫在眉睫的威胁。该项目选择1，4-二恶烷(二恶烷)作为模型污染物，并采用最先进的定向酶评估，以获得对基本细菌酶的基本生化见解，并最终优化它们对二恶烷的生物催化性能。定向酶进化将被用来模拟和加速实验室中的自然进化，创造出具有更高降解效率的酶突变株，以提高对二恶烷的降解效率。定向酶进化提供的丰富的经验数据集可以用来指导机器学习过程，以预测将蛋白质序列与其功能联系起来的关键分子决定因素，并为进一步改善其催化性能提出新的突变建议。这一综合框架将促进我们关于细菌酶生物化学的基础知识，并促进技术变革，以应对水中新出现的污染物造成的全球挑战。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。

The Environmental Chemical Science Program in the Division of Chemistry supports Professors Mengyan Li and Edgardo Farinas at the New Jersey Institute of Technology and Professor Wenwei Zheng at Arizona State University for this project. An increasing number of human-made chemicals are being released into the environment where they can deteriorate the water quality. Bacteria living in nature can secrete enzymes to degrade some of these chemicals and exploit them as carbon sources. However, the naturally occurring biodegradation process can be slow or inadequate to remove these contaminants. In this project, bacterial enzymes will be modified and optimized in the laboratory to speed up their degradation rates. The experimental results will be input data to train a computational model that simulates the interaction between the enzyme and the contaminant. In return, this computational model will promote the design of new enzymes with greater degradation rates. This project will initiate vigorous activities to engage graduate and undergraduate students, especially those who are members of underrepresented groups. Summer exchange workshops will be organized to promote communications between students and researchers from the New Jersey Institute of Technology and Arizona State University. Outreach efforts will be made to promote the green treatment of water contaminants and technology innovation by combining laboratory-based enzyme evaluation and computation-based machine learning.Some emerging contaminants pose imminent threats to public health and natural biota due to their frequent detection and enduring persistence in the environment. The project selects 1,4-dioxane (dioxane) as a model contaminant and employs state-of-the-art directed enzyme evaluation to gain fundamental biochemical insights into essential bacterial enzymes and ultimately optimize their biocatalytic performance for dioxane removal. Directed enzyme evolution will be used to mimic and accelerate the natural evolution in a laboratory setup, creating enzyme mutants with increased degradation efficiency towards dioxane. The rich empirical data set provided by directed enzyme evolution can be used to guide a machine learning process to predict key molecular determinants that link the protein sequence with its function and suggest new mutations for further improvement of their catalytic performance. This integrative framework will advance our fundamental knowledge regarding the biochemistry of bacterial enzymes and promote the technological transformation to combating the global challenges caused by emerging contaminants in water.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.