title: Data-driven approach to 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.
authors.affiliation: Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005, USA. calderon@rice.edu
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Calderon
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry*; D007700:Kinetics; D008956:Models, Chemical; D015233:Models, Statistical
chemical_list: D004798:Enzymes