  We present a novel machine learning approach to understanding conformation
dynamics of biomolecules. The approach combines kernel-based techniques that
are popular in the machine learning community with transfer operator theory for
analyzing dynamical systems in order to identify conformation dynamics based on
molecular dynamics simulation data. We show that many of the prominent methods
like Markov State Models, EDMD, and TICA can be regarded as special cases of
this approach and that new efficient algorithms can be constructed based on
this derivation. The results of these new powerful methods will be illustrated
with several examples, in particular the alanine dipeptide and the protein
NTL9.
