  Many aspects of the study of protein folding and dynamics have been affected
by the recent advances in machine learning. Methods for the prediction of
protein structures from their sequences are now heavily based on machine
learning tools. The way simulations are performed to explore the energy
landscape of protein systems is also changing as force-fields are started to be
designed by means of machine learning methods. These methods are also used to
extract the essential information from large simulation datasets and to enhance
the sampling of rare events such as folding/unfolding transitions. While
significant challenges still need to be tackled, we expect these methods to
play an important role on the study of protein folding and dynamics in the near
future. We discuss here the recent advances on all these fronts and the
questions that need to be addressed for machine learning approaches to become
mainstream in protein simulation.
