  Many processes of scientific importance are characterized by time scales that
extend far beyond the reach of standard simulation techniques. To circumvent
this impediment a plethora of enhanced sampling methods has been developed. One
important class of such methods relies on the application of a bias that is
function of a set of collective variables specially designed for the problem
under consideration. The design of good collective variables can be challenging
and thereby constitutes the main bottle neck in the application of these
methods. To address this problem, recently we have introduced Harmonic Linear
Discriminant Analysis, a method to systematically construct collective
variables. The method uses as input information on the metastable states
visited during the process that is being considered, information that can be
gathered in short unbiased MD simulations, to construct the collective
variables as linear combinations of a set of descriptors. Here, to scale up our
examination of the method's efficiency, we applied it to the folding of
Chignolin in water. Interestingly, already before any biased simulations were
run, the constructed one dimensional collective variable revealed much of the
physics that underlies the folding process. In addition, using it in
Metadynamics we were able to run simulations in which the system goes from the
folded state to the unfolded one and back, where to get fully converged results
we combined Metadynamics with Parallel Tempering. Finally, we examined how the
collective variable performs when different sets of descriptors are used in its
construction.
