Markov State Model (MSM)-based adaptive sampling protocols have been recently used to predict the long timescale behavior of proteins (1-7). Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways of several key cellular signaling proteins. The behavior of these proteins is controlled by molecular level interactions between specific sets of residues that relay the signal. We propose that atomistic simulations can help identify these key structural elements, and demonstrate this by examining the activation mechanism of several key cellular signaling proteins such as Kinases (1), G-Protein Coupled Receptors (2), Calmodulin (3), bacterial response regulators such as NtrC (4), Stress and energy signaling in plants (5) etc. These results have also provided us insights into both the biological function and evolutionary design of these key cellular signaling proteins. (6) In particular, we use sophisticated non-equilibrium statistical mechanical theory to probe how these signaling proteins do meaningful work with energy harnessed from their environment (7). Finally, these results show that MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions. 1. Shukla, D. Meng, Y. Roux, B. and Pande, V. S. Nature Communications, 5, 3397, 2014. 2. Kohlhoff K.*, and Shukla, D.* et al., Nature Chemistry, 6, 15-21, 2014. 3. Shukla, D. Peck, A. and Pande, V. S. Nature Communications, In press, 2015. 4. Vanatta D., Shukla D., Lawrenz M. and Pande, V. S., Nature Communications, 6, 7283, 2015. 5. Moffett A. S. and Shukla D., Nature Communications, In review, 2015. 6. Shukla D. et al., Accounts of Chemical Research, 48, 2, 414-422, 2015. 7. Weber, J. K. Shukla D. and Pande V.S., Proceedings of the National Academy of Sciences, 112 (33), 10377-10382, 2015.

Mechanistic insights into the activation mechanism of cellular signaling proteins

251st National Meeting (2016)

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