  Determining the different conformational states of a protein and the
transition paths between them is key to fully understanding the relationship
between biomolecular structure and function. This can be accomplished by
sampling protein conformational space with molecular simulation methodologies.
Despite advances in computing hardware and sampling techniques, simulations
always yield a discretized representation of this space, with transition states
undersampled proportionally to their associated energy barrier. We present a
convolutional neural network that learns a continuous conformational space
representation from example structures, and loss functions that ensure
intermediates between examples are physically plausible. We show that this
network, trained with simulations of distinct protein states, can correctly
predict a biologically relevant non-linear transition path, without any example
on the path provided. We also show we can transfer features learnt from one
protein to others, which results in superior performances, and requires a
surprisingly small number of training examples.
