On the Use of Local Diffusion Models for Path Ensemble Averaging in Potential of Mean Force Computations

Abstract

We use a constant velocity steered molecular dynamics (SMD) simulation of the stretching of deca-alanine in vacuum to demonstrate a technique that can be used to create surrogate stochastic processes using the time series that come out of SMD simulations. The surrogate processes are constructed by first estimating a sequence of local parametric models along a SMD trajectory and then a single global model is constructed by piecing the local models together through smoothing splines (estimation is made computationally feasible by likelihood function approximations). The calibrated surrogate models are then "bootstrapped" in order to simulate the large number of work paths typically needed to construct a potential of mean force (PMF) by appealing to Jarzynski's work theorem. When this procedure is repeated for a small number of SMD paths, it is shown that the global models appear to come from a single family of closely related diffusion processes. Possible techniques for exploiting this observation are also briefly discussed. The findings of this paper have potential relevance to computationally expensive computer simulations and experimental works involving optical tweezers where it difficult to collect a large number of samples, but possible to sample accurately and frequently in time.

working paper (33 pages, 11 figures)

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Cited by in corpus (5)

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• Fitting Effective Diffusion Models to Data Associated with a "Glassy Potential": Estimation, Classical Inference Procedures and Some Heuristics
• Approximating nonequilibrium processes using a collection of surrogate diffusion models
• Correcting for Bias of Molecular Confinement Parameters Induced by Small Time Series Sample Sizes in Single-Molecule Trajectories Containing Measurement Noise
• A Random Force is a Force, of Course, of Coarse: Decomposing Complex Enzyme Kinetics with Surrogate Models