The central theme of our research is to develop and apply novel theoretical methods to understand the physical properties of biological molecules, such as proteins, nucleic acids, and lipid membranes, and to apply this understanding to design novel synthetic systems, including small molecule therapeutics. In particular, we are interested in the self-assembly properties of biomolecules: for example, how do protein and RNA molecules fold? How do proteins misfold and aggregate and how can we use our understanding of this process to tackle misfolding related diseases, such as Alzheimer's or Huntington's Disease? How can we design or discover novel small molecules to inhibit this process? As these phenomena are complex, spanning from the molecular to mesoscopic length scales and the nanosecond to millisecond timescales, our research employs a variety of methods, including statistical mechanical analytic models, Markov State Models, and statistical and informatic methods, as well as Monte Carlo, Langevin dynamics, and molecular dynamics computer simulations on workstations and massively parallel supercomputers, superclusters, and large-scale worldwide distributed computing (see http://folding.stanford.edu). Our work also touches closely in parts with applications of Bayesian statistics to statistical mechanics, as well as novel means for computational small molecule (drug) design (such as novel methods for docking and free energy calculation).
Condensed Matter Physics
My research centers around the statistical mechanics of soft materials, including proteins, DNA, and lipid membranes.
- The predicted structure of the headpiece of the Huntingtin protein and its implications on Huntingtin aggregation
- Inside the Chaperonin toolbox: Theoretical and computational models for chaperonin mechanism
- Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint
- Progress and Challenges in the Automated Construction of Markov State Models for Full Protein Systems
- Charge, hydrophobicity, and confined water: Putting past simulations into a simple theoretical framework
- Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39)
- Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models
- Protein folded states are kinetic hubs
- Bayesian inference for Brownian dynamics
- A Simple Theory of Protein Folding