Towards Improving Sampling and Understanding of Biomolecular Information in Molecular Dynamics Calculations

Tom Woolf, Johns Hopkins University

Molecular dynamics computer calculations with modern hardware applied to biological molecules can now reach trajectory lengths up to microseconds in length. Yet, to couple these simulations to grand-challenge problems in medicine and biology: genomics, metabolomics, signaling events, and drug design, still more sampling of the conformational space available to biomolecules is needed. This creates challenges for computer algorithms and data structures to go beyond the current approach and find new ways to sample and to organize the information.

This talk will describe two algorithms and recent work in data-structures that may address the sampling problems. The dynamic importance sampling method grows out of Monte Carlo importance sampling to dynamically enhance sampling of intermediate conformations between stable states. The effective transfer entropy method may provide reduced dimensionality projections to further enhance sampling. These directions are coupled with suggestions for control and hierarchy in data-structures to aid the more efficient and information-rich collection of molecular trajectories.

Speaker Biography

Tom Woolf has a physics background, with a BS in Physics from Stanford, MS from the University of Chicago, and a PhD from Yale University in Biophysics/Neuroscience. His post-doctoral work was on molecular dynamics simulations of the gramicidin channel and he’s been pursuing molecular dynamics simulations since arriving at Hopkins in 1994. He is currently a Professor at the School of Medicine in the Department of Physiology. His research has pursued studies aimed at structure:function questions for membrane proteins, at relative free energy calculations for ligand binding, and at the kinetics of conformational change. This later research direction has led to his interest in how to most efficiently explore and understand conformational space.