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

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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.