Progress and challenges in the automated construction of Markov state models for full protein systems.
Markov state models (MSMs) are a powerful tool for modeling both the thermodynamics and kinetics of molecular systems. In addition, they provide a rigorous means to combine information from multiple sources into a single model and to direct future simulations/experiments to minimize uncertainties in the model. However, constructing MSMs is challenging because doing so requires decomposing the extremely high dimensional and rugged free energy landscape of a molecular system into long-lived states, also called metastable states. Thus, their application has generally required significant chemical intuition and hand-tuning. To address this limitation we have developed a toolkit for automating the construction of MSMs called MSMBUILDER (available at https://simtk.org/home/msmbuilder). In this work we demonstrate the application of MSMBUILDER to the villin headpiece (HP-35 NleNle), one of the smallest and fastest folding proteins. We show that the resulting MSM captures both the thermodynamics and kinetics of the original molecular dynamics of the system. As a first step toward experimental validation of our methodology we show that our model provides accurate structure prediction and that the longest timescale events correspond to folding.
Biophysics Program, Stanford University, Stanford, California 94305, USA.
Gregory R
GR
Bowman
Kyle A
KA
Beauchamp
George
G
Boxer
Vijay S
VS
Pande
D003198:Computer Simulation; D008390:Markov Chains; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*
D011506:Proteins