B.A., 1992, Princeton University
Ph.D. 1995, Massachusetts Institute of Technology
Postdoctoral Associate, 1995-1996, Massachusetts Institute of Technology, Physics Department and Center for Materials Science & Engineering; Miller Fellow, 1996-1999, University of California at Berkeley, Physics Department; Levinthal Lecture, (OpenEye CUP II), 2002; MIT Tech Review Top 100 Young Innovators, 2002; Dreyfus Teacher-Scholar Award, 2003; Global Indus Technovators Award, 2004; Keynote Speaker, HiCOMB 2005; Keynote Speaker, HPDC-15, 2006; Irving Sigal Young Investigator Award, Protein Society, 2006; Fellow, American Physical Society, 2008; Michael and Kate Barany Award for Young Investigators, 2012; DeLano Award for Computational Biosciences from AABMB, 2015
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, GPUs, 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).
For example, we are currently investigating the nature of protein folding and misfolding, relevant for diseases such as Alzheimer's and Huntington's Disease. We have performed simulations of these processes, in all-atom detail on experimentally relevant timescales (milliseconds to seconds), yielding specific predictions of the structural and physical chemical nature of protein aggregation involved in these diseases. These simulation results have then fed into novel computational small molecule drug design methods, yielding novel chemical entities with important and interesting properties.
Since such problems are extremely computationally demanding, we have developed distributed computing projects for protein folding dynamics ("Folding@Home": http://folding.stanford.edu) which has attracted over 8,000,000 PCs since the project's beginning in October 1, 2000 and today is recognized as one of the most powerful supercomputers/superclusters in the world. Such enormous computational resources have allowed us to simulate unprecedented folding timescales (microseconds to milliseconds) and statistical precision and accuracy (such as very accurate and precise free energy calculations). For more details, please see the Folding@Home Project page.
2) “A Simple Theory of Protein Folding,” V.S. Pande, Physical Review Letters, 105, 198101 (2010)
3) “Protein folded states are kinetic hubs,” G. Bowman and V.S. Pande, Proceedings of the National Academy of Sciences, USA 107, 10890-10895 (2010)
4) “Atomistic folding simulations of the five helix bundle protein λ6-85,” G. Bowman, V. Voelz, and V.S. Pande, Journal of the American Chemical Society, 133, 664-667 (2011)
5) “MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale,” K. Beauchamp, G.R. Bowman, T.J. Lane, L. Maibaum, I.S. Haque, and V.S. Pande, Journal of Computational and Theoretical Chemistry, 7, 3412-3419 (2011).
6) “Splitting probabilities as a test of reaction coordinate choice in single-molecule experiments,” J. Chodera and V.S. Pande, Physical Review Letters, 107, 098102 (2011)
7) “A smoothly decoupled particle interface (SDPI): new methods for coupling explicit and implicit solvent,” J. Wagoner and V.S. Pande, Journal of Chemical Physics, 134, 214103 (2011)
8) “Large-scale Chemical Informatics on the GPU,” I. Haque and V.S. Pande, GPU Gems (2010)
9) “Everything you wanted to know about Markov State Models but were afraid to ask,” V.S. Pande, K. Beauchamp, and G.R. Bowman, Methods, 52, 99-105 (2010)
10) "Screen Savers of the World, Unite!" M.R. Shirts and V.S. Pande, Science, 290, 1903-1904 (2000)