  We present a machine learning framework for modeling protein dynamics. Our
approach uses L1-regularized, reversible hidden Markov models to understand
large protein datasets generated via molecular dynamics simulations. Our model
is motivated by three design principles: (1) the requirement of massive
scalability; (2) the need to adhere to relevant physical law; and (3) the
necessity of providing accessible interpretations, critical for both cellular
biology and rational drug design. We present an EM algorithm for learning and
introduce a model selection criteria based on the physical notion of
convergence in relaxation timescales. We contrast our model with standard
methods in biophysics and demonstrate improved robustness. We implement our
algorithm on GPUs and apply the method to two large protein simulation datasets
generated respectively on the NCSA Bluewaters supercomputer and the
Folding@Home distributed computing network. Our analysis identifies the
conformational dynamics of the ubiquitin protein critical to cellular
signaling, and elucidates the stepwise activation mechanism of the c-Src kinase
protein.
