  The modeling of atomistic biomolecular simulations using kinetic models such
as Markov state models (MSMs) has had many notable algorithmic advances in
recent years. The variational principle has opened the door for a nearly fully
automated toolkit for selecting models that predict the long-time kinetics from
molecular dynamics simulations. However, one yet-unoptimized step of the
pipeline involves choosing the features, or collective variables, from which
the model should be constructed. In order to build intuitive models, these
collective variables are often sought to be interpretable and familiar
features, such as torsional angles or contact distances in a protein structure.
However, previous approaches for evaluating the chosen features rely on
constructing a full MSM, which in turn requires additional hyperparameters to
be chosen, and hence leads to a computationally expensive framework. Here, we
present a method to optimize the feature choice directly, without requiring the
construction of the final kinetic model. We demonstrate our rigorous
preprocessing algorithm on a canonical set of twelve fast-folding protein
simulations, and show that our procedure leads to more efficient model
selection.
