CAREER: New strategy to outsmart antimicrobial resistance: Mastering evolution of beta-lactamases catalytic mechanism through reaction pathways alignment, simulation, and analysis

南方卫理公会大学的彭涛得到了化学部化学理论、模型和计算方法计划的支持，该计划开发新的计算方法来建模和预测导致危险抗生素耐药菌株的β-内酰胺酶催化机制的演变。对于地球上所有的生命系统来说，蛋白质是如何进化的是一个重要的问题。以前对蛋白质序列和结构的研究为蛋白质进化提供了很好的洞察力。然而，对于蛋白质的作用机制，尤其是对酶的作用机制，人们缺乏了解。这阻碍了对蛋白质进化的更深入理解。为了迎接这一挑战，陶教授和他的研究小组正在开发一种先进的理论框架和计算技术，将酶序列和结构的进化与其功能、催化活性和机制的进化联系起来。他们将重点放在β-内酰胺酶上，这种酶可以分解β-内酰胺类抗生素，是产生抗菌素耐药性的主要原因之一。这项工作将从根本上了解β-内酰胺酶的催化机制的演变，并可能有助于开发新一代低耐药性抗生素。该奖项还支持开发教育材料，以促进基础科学教育和社交媒体上的宣传。这些免费获得的材料将造福和塑造下一代来自经济困难学生群体的跨学科科学家和工程师。该项目专注于开发一个理论框架来描述酶机制景观，其定义是使用统计和机器学习方法对进化上相关的酶的排列反应路径进行结构分析。这一理论框架有助于预测酶的新突变如何导致催化机制和活性的变化。陶教授和他的研究小组正在开发新的计算方法，以探索酶结构-功能之间的关系，并提供蛋白质结构进化和酶机制变化之间缺失的联系，这些联系不太适合实验。他们还在开发基于在线的化学教育渠道和推广平台，通过与其他学校的教师合作来加强普通化学教育。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。

Peng Tao of Southern Methodist University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational methods to model and predict the evolution of beta-lactamase catalytic mechanisms that lead to dangerous antibiotic resistant strains. How proteins are evolved is a vital question to answer for all living systems on earth. Previous studies focusing on protein sequences and structures provided great insight into protein evolution. However, there is a lack of understanding about functional mechanisms for proteins, especially for enzymes. This prevents deeper understanding of protein evolution. To meet this challenge, Professor Tao and his research group are developing an advanced theoretical framework and computational techniques to connect the evolution of enzyme sequences and structure to the evolution of their functions, catalytic activity, and mechanisms. They focus on beta-lactamases as enzymes that hydrolyze beta-lactam antibiotics and serve as one main cause of antimicrobial resistance. This work will provide fundamental understanding about the evolution of beta-lactamases' catalytic mechanisms, and could help develop a new generation of antibiotics with low resistance. The award also supports the development of educational materials to boost basic science education and outreach on social media. These freely accessible materials will benefit and shape the next generation of interdisciplinary scientists and engineers from economically disadvantaged student populations.The project focuses on developing a theoretical framework to describe enzymatic mechanism landscapes, which are defined as the structural analysis of aligned reaction pathways of evolutionarily related enzymes using statistical and machine-learning methods. This theoretical framework is helping to predict how new mutations in enzymes could lead to changes in catalytic mechanisms and activities. New computational methods are being developed by Professor Tao and his research group to explore enzyme structure-function relations and provide the missing connection between the evolution of protein structure and the variations in enzyme mechanism that are less amenable to experiment. They are also developing online-based chemistry education channels and outreach platforms to enhance general chemistry education through collaboration with teachers from other schools.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.