Our goal is to understand the role of protein dynamics and allostery in human health and disease, empowering both our lab and others to combat global health threats like Alzheimer’s disease and COVID-19 by informing the development of new drugs and proteins.
To rapidly converge on predictive models, we iterate between using simulations to gain mechanistic insight, conducting our own experimental tests of our models, and refining our simulations/analysis based on feedback from experiments.
We also develop new methods to access phenomena that are otherwise beyond reach, such as the mechanisms of allosteric communication between distant regions of a protein. For example, we run the Folding@home distributed computing project (one of the world’s largest computational resources) to enable simulations on an unprecedented scale. We also develop Markov state model (MSM) methods, adaptive sampling algorithms, and machine learning tools to efficiently build maps of protein dynamics and extract valuable insights.
To achieve our ambitious objectives, we work with a wide network of collaborators. Here is a word from some of the people and organizations that supported our recent work on COVID-19.