Prinz, J.H. and Chodera, J. D. and Pande, V. S. and Swope, W. D. and Smith, J. C. and Noé, F. (2011) Optimal use of data in parallel tempering simulations for the construction of discretestate Markov models of biomolecular dynamics. J. Chem. Phys., 134 (24). p. 244108.

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Official URL: http://dx.doi.org/10.1063/1.3592153
Abstract
Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperaturedependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperaturedependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide.
Item Type:  Article 

Subjects:  Physical Sciences 
Divisions:  Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Molecular Biology Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group 
ID Code:  993 
Deposited By:  BioComp Admin 
Deposited On:  17 Nov 2010 15:41 
Last Modified:  03 Mar 2017 14:40 
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