  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.
