Research


Ion channels

Sodium channels initiate signaling in neurons and other cells which go on to become sensations of pleasure and pain, as well as thoughts and feelings. In a collaboration with the duBois lab and with the help of Folding@Home, we are launching a large scale molecular dynamics study of the voltage gated sodium channel.

We’re studying the dynamics of transitions between the open and closed states mediated by the voltage sensing domains. Our experimental collaborators will probe sodium channel function through the use of natural toxins secreted by frogs in the Amazon rain forest. By combining theory and experiment, we can propose and test derivatives of these toxins which have positive therapeutic effects.

Markov Modelling

Our lab develops and applies novel statistical techniques to understand and interpret the huge volume of data returned from a molecular dynamics study. Particularly, we use Markov State Models (MSMs) which model conformational dynamics as a series of memoryless jumps between microstates.

Solvent dynamics

I’ve introduced a new method for including solvent degrees of freedom in MSM analysis. Check out the paper introducing the method and the code. We’re working on applying it to new and exciting systems.

Adaptive sampling

MSMs offer a convenient framework for analyzing the results of simulations, but they can also be used to direct simulation. In an adaptive sampling scheme, uncertainty in particular MSM state transition probabilities are used to seed new simulations to speed convergence of the model.

Papers

  1. Conserve Water: A Method for the Analysis of Solvent in Molecular Dynamics
    Matthew P. Harrigan; Diwakar Shukla; Vijay S. Pande
    February 12, 2015
    J. Chem. Theory Comput., 2015, 11 (3), pp 1094--1101. doi:10.1021/ct5010017 [pdf]
  2. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
    Robert T. McGibbon; Kyle A. Beauchamp; Matthew P. Harrigan; Christoph Klein; Jason M. Swails; Carlos X. Hernández; Christian R. Schwantes; Lee-Ping Wang; Thomas J. Lane; Vijay S. Pande
    October 20, 2015
    Biophys. J., 109, 8, pp 1528--1532. doi:10.1016/j.bpj.2015.08.015 [bioRxiv] [pdf]
  3. User-friendly software for analyzing MD simulations
    December 01, 2015
    Nature Methods 12, 1116. doi:10.1038/nmeth.3669
  4. Osprey: Hyperparameter Optimization for Machine Learning
    Robert T. McGibbon; Carlos X. Hernández; Matthew P. Harrigan; Steven Kearnes; Mohammad M. Sultan; Stanislaw Jastrzebski; Brooke E. Husic; Vijay S. Pande
    September 07, 2016
    JOSS, 1, 5. doi:10.21105/joss.00034
  5. MSMBuilder: Statistical Models for Biomolecular Dynamics
    Matthew P. Harrigan; Mohammad M. Sultan; Carlos Xavier Hernandez; Brooke E. Husic; Peter Eastman; Christian R. Schwantes; Kyle A. Beauchamp; Robert T. McGibbon; Vijay S. Pande
    January 10, 2017
    Biophys. J., 112, 10--15. doi:10.1016/j.bpj.2016.10.042 [bioRxiv]
  6. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics
    Peter Eastman; Jason Swails; John D. Chodera; Robert T. McGibbon; Yutong Zhao; Kyle A. Beauchamp; Lee-Ping Wang; Andrew C. Simmonett; Matthew P. Harrigan; Bernard R. Brooks; Vijay S. Pande
    December 06, 2016
    bioRxiv. doi:10.1101/091801
  7. Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter
    Matthew P. Harrigan; Keri A. McKiernan; Veerabahu Shanmugasundaram; Rajiah Aldrin Denny; Vijay S. Pande
    December 07, 2016
    bioRxiv. doi:10.1101/092155