(3de) Understanding and Exploiting Protein Functional Dynamics to Combat Drug Resistance

Resistance to antibiotics and other drugs is increasing rapidly but pharmaceutical companies are investing less and less in combating this looming threat to human health precisely because resistance is likely to emerge before they can recoup their costs, much less make a profit.  I propose to use a combination of theory, simulation, and experiment to understand the biophysical basis of drug resistance and develop new therapeutic strategies that will make combating resistance more tractable.  I am uniquely suited for this work because of my strong background in understanding how a molecule’s free energy landscape determines its structure, dynamics, and function and then using perturbations (like drugs) to manipulate these properties. 

During my doctoral work at Stanford, I developed computational methods for extending the reach of atomistic models from hundreds of nanoseconds timescales to tens of milliseconds and applied them to develop a new theory of protein folding.  The models I developed—called Markov state models (MSMs)—are discrete-time master equation models built from extensive molecular dynamics simulations using a combination of physics, Bayesian statistics, information theory, and network theory.  They are essentially maps of the conformational space a molecule explores that capture both its thermodynamics and kinetics. 

As a Miller Research Fellow at UC Berkeley, I am adapting/applying my methods to understand aspects of protein function like allostery, ligand binding, and protein-protein interactions.  A major emphasis of my work is predicting how perturbations (like drugs and mutations) can alter a protein’s function and then testing these predictions experimentally.