  Recent advances in deep learning frameworks have established valuable tools
for analyzing the long-timescale behavior of complex systems such as proteins.
Especially the inclusion of physical constraints, e.g. time-reversibility, was
a crucial step to make the methods applicable to biophysical systems.
Furthermore, we advance the method by incorporating experimental observables
into the model estimation showing that biases in simulation data can be
compensated for. We further develop a new neural network layer in order to
build an hierarchical model allowing for different level of details to be
studied. Finally, we propose an attention mechanism which highlights important
residues for the classification into different states. We demonstrate the new
methodology on an ultralong molecular dynamics simulation of the Villin
headpiece miniprotein.
