Two major sources of error characterize a Markov state model. The first is the convergence of the dataset -- we can only model the processes that we've simulated, in some form or another. When the sampling is insufficient, its not like the MSM can make something out of nothing. This ...

A few of the projects on my mind right now. Some are father along than others.

Build custom OpenMM scripts right in the browser! OpenMM is one of the most flexible molecular dynamics packages, put it can be a little intimidating for the new user. Instead of interacting with it via a set of command line scripts, as one would with amber or gromacs, to interact ...

My first first-author paper has just been accepted by the Journal of Chemical Theory and Computation. The title is Learning Kinetic Distance Metrics for Markov State Models of Protein Conformational Dynamics.

A few weeks ago, I gave the Pande Group meeting. The slides are on github, and you can view them here. The title of the talk is Protein Folding is Easy: Towards Markov State Models for Conformational Change, and mostly addresses my learning distance metrics for kinetic clustering of protein ...