- This event has passed.
MICDE Seminar: Heather Mayes, Chemical Engineering, University of Michigan
February 20 @ 2:00 pm - 3:00 pm
Bio: Heather Mayes is an Assistant Professor in the Department of Chemical Engineering. Her research group uses multi-scale modeling to discover protein-sugar interactions and to harness them for renewable energy and improved health. The study of carbohydrate-protein interactions is an important step to create renewable fuels and chemicals from non-food biomass, and the results can be applied to several human diseases, including cancer and autoimmune disorders. Prof. Mayes uses computational tools in her research, including quantum mechanics, molecular dynamics, and rare-event sampling methods. She collaborates with experimental groups to understand past and guide future wet-lab studies to advance renewable chemicals and fuels, as well as disease understanding.
Simulating Protein-Carbohydrate Interactions to Bridge the Gap Between Human Chemical Intuition and Molecular Biophysics
In complex reacting systems, it can be exceedingly difficult, or even impossible, to tease out elementary reaction mechanisms from wet-lab data alone, due to data convolution resulting from the multiple reacting steps and competing reactions that simultaneously occur. The systems that the Mayes group studies (multiple types of protein-carbohydrate interactions) certainly fall into this category, with understanding further hindered by the conformational, stereochemical, and regiochemical degrees of freedom key to chemical reactions in these systems. Yet, understanding these elementary mechanisms would not only help answer fundamental questions in biology, but also improve our ability to harness these systems for applications from renewable energy to pharmaceutical interventions. I will discuss several systems that we are studying, and focus on our investigations of how enzymes break down plant biomass. I will share how our computational research rationalizes non-intuitive wet-lab observations by revealing mechanisms that do not conform to human intuition. In doing so, we gather lessons from how nature has evolved efficient enzymes that we can then apply to rational enzyme design.