Front cover image for Statistical models of protein conformational dynamics

Statistical models of protein conformational dynamics

Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) -- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics -- is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, statically-sound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms
Thesis, Dissertation, English, 2016
Stanford University