Office: JSCBB C123
Mailbox: 596 UCB
AB, Harvard University (1999)
PhD, Stanford University (2005)
NIH Ruth L. Kirschstein-NRSA Fellow, Columbia University (2005-2008)
Understanding and designing materials at the molecular scale:
We work with experimentalists to better understand the molecular details of protein purification materials, water filtration membranes, and nanotextured surfaces, and to suggest novel improvements. The wide physical and chemical diversity of biomolecular processes strongly suggests that the possibilities for novel function in human-engineered materials are far, far beyond our current capabilities. Designed materials can draw from a much larger range of chemical structure and functionality than exists biologically; if we can add significant chemical diversity to nature's already impressive toolkit, what else can be created? Computational approaches provide a vitally important tool to help explore this enormously large materials space.
Computed-aided drug design:
Drug resistance is one of the biggest challenges in the pharmacological treatment of infectious diseases, and current informatics based drug discovery methods are not well-suited to rapidly develop new drug variants that can successfully overcome resistance. Our research has demonstrated that statistical mechanical methods can predict ligand binding affinities to within 1 kcal/mol in simple atomistically detailed systems, a level that becomes useful for the pharmaceutical industry. However, significant effort is necessary to make such methods work in more typical drug systems and to make them scale efficiently enough to be useful in general practice. We work together with experimental researchers, software developers, and pharmaceutical companies to make computational drug design a reality, helping bring down the cost of developing cures to a host of diseases.
Improvements in molecular simulation and property prediction for engineering:
The two most pressing problems holding back improved atomic-level simulation of polymers, macromolecules, and other complicated dense fluids are the lack of sufficient sampling to accurately measure and observe molecular phenomena, and the choice of model parameters used to perform the simulations. It is currently only possible to simulate the equivalent of a few microseconds of all but the smallest biological systems, with some heroically expensive extensions to milliseconds with large supercomputers. Without sufficient conformational sampling, it is impossible even to verify if models are sufficiently faithful to experiment, let alone explore behavior of either long time scales or of larger molecular systems. Without good model parameters, atomistic predictions are unreliable and misleading, and developing new parameters is a labor-intensive process with significant guesswork. In the Shirts group, we research improved configurational sampling methods for macromolecules and more automated, statistically based methods for choosing molecular model parameters. These tools have the potential to assist researchers performing molecular simulation in all fields.