%0 Journal Article %1 Bowman2009MarkovStateModels %A Bowman, Gregory R. %A Beauchamp, Kyle A. %A Boxer, George %A Pande, Vijay S. %D 2009 %J The Journal of Chemical Physics %K energy-landscape markov-state-models msmbuilder software %N 12 %R http://dx.doi.org/10.1063/1.3216567 %T Progress and challenges in the automated construction of Markov state models for full protein systems %U http://scitation.aip.org/content/aip/journal/jcp/131/12/10.1063/1.3216567 %V 131 %X 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.

@article{Bowman2009MarkovStateModels, abstract = {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.}, added-at = {2016-06-04T23:20:23.000+0200}, author = {Bowman, Gregory R. and Beauchamp, Kyle A. and Boxer, George and Pande, Vijay S.}, biburl = {https://www.bibsonomy.org/bibtex/25865340bac0b7207779c272d91db123e/salotz}, description = {Progress and challenges in the automated construction of Markov state models for full protein systems}, doi = {http://dx.doi.org/10.1063/1.3216567}, eid = {124101}, interhash = {59f1bc3260e649fc2ab04e1a3fb512c7}, intrahash = {5865340bac0b7207779c272d91db123e}, journal = {The Journal of Chemical Physics}, keywords = {energy-landscape markov-state-models msmbuilder software}, number = 12, timestamp = {2016-06-04T23:35:32.000+0200}, title = {Progress and challenges in the automated construction of Markov state models for full protein systems}, url = {http://scitation.aip.org/content/aip/journal/jcp/131/12/10.1063/1.3216567}, volume = 131, year = 2009 }