  A molecular understanding of how protein function is related to protein
structure will require an ability to understand large conformational changes
between multiple states. Unfortunately these states are often separated by high
free energy barriers and within a complex energy landscape. This makes it very
difficult to reliably connect, for example by all-atom molecular dynamics
calculations, the states, their energies and the pathways between them. A major
issue needed to improve sampling on the intermediate states is an order
parameter -- a reduced descriptor for the major subset of degrees of freedom --
that can be used to aid sampling for the large conformational change. We
present a novel way to combine information from molecular dynamics using
non-linear time series and dimensionality reduction, in order to quantitatively
determine an order parameter connecting two large-scale conformationally
distinct protein states. This new method suggests an implementation for
molecular dynamics calculations that may dramatically enhance sampling of
intermediate states.
