Approximating nonequilibrium processes using a collection of surrogate diffusion models
arXiv:0801.4281 · doi:10.1063/1.2903439
Abstract
The surrogate process approximation (SPA) is applied to model the nonequilibrium dynamics of a reaction coordinate (RC) associated with the unfolding and refolding processes of a deca-alanine peptide at 300 K. The RC dynamics, which correspond to the evolution of the end-to-end distance of the polypeptide, are produced by steered molecular dynamics (SMD) simulations and approximated using overdamped diffusion models. We show that the collection of (estimated) SPA models contain structural information "orthogonal" to the RC monitored in this study. Functional data analysis ideas are used to correlate functions associated with the fitted SPA models with the work done on the system in SMD simulations. It is demonstrated that the shape of the nonequilibrium work distributions for the unfolding and refolding processes of deca-alanine can be predicted with functional data analysis ideas using a relatively small number of simulated SMD paths for calibrating the SPA diffusion models.
13 pages, 7 figures
References in corpus (10)
- A nonequilibrium equality for free energy differences
- Verification of the Crooks fluctuation theorem and recovery of RNA folding free energies
- The Nonequilibrium Thermodynamics of Small Systems
- Rare events and the convergence of exponentially averaged work values
- Experimental Free Energy Surface Reconstruction From Single-Molecule Force Spectroscopy Using Jarzynski's Equality
- Calculating potentials of mean force and diffusion coefficients from nonequilibirum processes without Jarzynski's equality
- Parameter Estimation for Multiscale Diffusions
- The Steady State Fluctuation Relation for the Dissipation Function
- On the Use of Local Diffusion Models for Path Ensemble Averaging in Potential of Mean Force Computations
- Fitting Effective Diffusion Models to Data Associated with a "Glassy Potential": Estimation, Classical Inference Procedures and Some Heuristics