We describe new methods for modeling thermodynamics and kinetics from simulation data and introduce to our last release of PyEMMA - http://pyemma.org. The main part of the talk discusses statistically optimal ways to estimate both thermodynamics (free energies, equilibrium probabilities) and kinetics (transition rates, relaxation timescales) from data at multiple thermodynamic states, such as umbrella sampling or replica-exchange simulations, or combinations of these with direct MD simulations. Such statistically optimal estimates can be obtained by transition-based reweighting analysis methods (TRAM). [1] Wu, H., Mey, A.S.J.S., Rosta, E. and Noé, F.: Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states. J. Chem. Phys., 141. 214106 (2014). [2] Scherer, M.K., Trendelkamp-Schroer, B., Paul F., Perez-Hernandez G., Hoffmann, M., Plattner, N., Prinz J.-H. and Noe, F.: PyEMMA 2: A software package for estimation, validation and analysis of Markov models. J. Chem. Theory Comput. (in press). [3] Plattner, N. and Noé, F.: Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models. Nat. Commun. 6. 7653 (2015).

Markov models at multiple thermodynamic states with applications to protein-ligand complexes

251st National Meeting (2016)

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