  Markov State Models (MSMs) are a powerful framework to reproduce the
long-time conformational dynamics of biomolecules using a set of short
Molecular Dynamics (MD) simulations. However, precise kinetics predictions of
MSMs heavily rely on the features selected to describe the system. Despite the
importance of feature selection for large system, determining an optimal set of
features remains a difficult unsolved problem. Here, we introduce an automatic
approach to optimize feature selection based on genetic algorithms (GA), which
adaptively evolves the most fitted solution according to natural selection
laws. The power of the GA-based method is illustrated on long atomistic folding
simulations of four proteins, varying in length from 28 to 80 residues. Due to
the diversity of tested proteins, we expect that our method will be extensible
to other proteins and drive MSM building to a more objective protocol.
