A Random Force is a Force, of Course, of Coarse: Decomposing Complex Enzyme Kinetics with Surrogate Models

Abstract

The temporal autocorrelation (AC) function associated with monitoring order parameters characterizing conformational fluctuations of an enzyme is analyzed using a collection of surrogate models. The surrogates considered are phenomenological stochastic differential equation (SDE) models. It is demonstrated how an ensemble of such surrogate models, each surrogate being calibrated from a single trajectory, indirectly contains information about unresolved conformational degrees of freedom. This ensemble can be used to construct complex temporal ACs associated with a "non-Markovian" process. The ensemble of surrogates approach allows researchers to consider models more flexible than a mixture of exponentials to describe relaxation times and at the same time gain physical information about the system. The relevance of this type of analysis to matching single-molecule experiments to computer simulations and how more complex stochastic processes can emerge from a mixture of simpler processes is also discussed. The ideas are illustrated on a toy SDE model and on molecular dynamics simulations of the enzyme dihydrofolate reductase.

11 pages / 6 figures

References in corpus (9)

• Stochastic modeling in nanoscale biophysics: Subdiffusion within proteins
• Coarse-Grained Kinetic Computations for Rare Events: Application to Micelle Formation
• A test for model specification of diffusion processes
• Inherent flexibility determines the transition mechanisms of the EF-hands of Calmodulin
• Nonlinear stochastic models of 1/f noise and power-law distributions
• Optimal Langevin modelling of out-of-equilibrium molecular dynamics simulations
• 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
• Approximating nonequilibrium processes using a collection of surrogate diffusion models