个人简介

Education: B.A., 1992, Princeton University; Ph.D.,1995, Massachusetts Institute of Technology Awards: 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

研究领域

Biophysical/Physical/Theoretical

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.

近期论文

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Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model. J Phys Chem B. 2015 Feb 26; Authors: Laury ML, Wang LP, Pande VS, Head-Gordon Markov state models provide insights into dynamic modulation of protein function. Acc Chem Res. 2015 Feb 17;48(2):414-22 Authors: Shukla D, Hernández CX, Weber JK, Cloud computing approaches for prediction of ligand binding poses and pathways. Sci Rep. 2015;5:7918 Authors: Lawrenz M, Shukla D, Pande VS Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations. J Chem Theory Comput. 2014 Dec 9;10(12):5217-5223 Authors: Sultan MM, Kiss G, Shukla D, Pande VS Discovering chemistry with an ab initio nanoreactor. Nat Chem. 2014 Dec;6(12):1044-8 Authors: Wang LP, Titov A, McGibbon R, Liu F, Pande VS, Martínez Perspective: Markov models for long-timescale biomolecular dynamics. J Chem Phys. 2014 Sep 7;141(9):090901 Authors: Schwantes CR, McGibbon RT, Pande VS Dynamical phase transitions reveal amyloid-like states on protein folding landscapes. Biophys J. 2014 Aug 19;107(4):974-82 Authors: Weber JK, Jack RL, Schwantes CR, Pande VS Complex pathways in folding of protein G explored by simulation and experiment. Biophys J. 2014 Aug 19;107(4):947-55 Authors: Lapidus LJ, Acharya S, Schwantes CR, Wu L, Shukla Statistical model selection for Markov models of biomolecular dynamics. J Phys Chem B. 2014 Jun 19;118(24):6475-81 Authors: McGibbon RT, Schwantes CR, Pande VS Bayesian energy landscape tilting: towards concordant models of molecular ensembles. Biophys J. 2014 Mar 18;106(6):1381-90 Authors: Beauchamp KA, Pande VS, Das R A molecular interpretation of 2D IR protein folding experiments with Markov state models. Biophys J. 2014 Mar 18;106(6):1359-70 Authors: Baiz CR, Lin YS, Peng CS, Beauchamp KA, Voelz Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat Commun. 2014;5:3397 Authors: Shukla D, Meng Y, Roux B, Pande VS Long Timestep Molecular Dynamics on the Graphical Processing Unit. J Chem Theory Comput. 2013 Aug 13;9(8):3267-3281 Authors: Sweet JC, Nowling RJ, Cickovski T, Sweet Finite domain simulations with adaptive boundaries: accurate potentials and nonequilibrium movesets. J Chem Phys. 2013 Dec 21;139(23):234114 Authors: Wagoner JA, Pande VS Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem. 2014 Jan;6(1):15-21 Authors: Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Inferring the rate-length law of protein folding. PLoS One. 2013;8(12):e78606 Authors: Lane TJ, Pande VS Calculations of the electric fields in liquid solutions. J Phys Chem B. 2013 Dec 19;117(50):16236-48 Authors: Fried SD, Wang LP, Boxer SG, Understanding protein folding using Markov state models. Adv Exp Med Biol. 2014;797:101-6 Authors: Pande VS Introduction and overview of this book. Adv Exp Med Biol. 2014;797:1-6 Authors: Bowman GR, Pande VS, Noé F SCISSORS: practical considerations. J Chem Inf Model. 2014 Jan 27;54(1):5-15 Authors: Kearnes SM, Haque IS, Pande VS