  Deep Learning (DL) algorithms hold great promise for applications in the
field of computational biophysics. In fact, the vast amount of available
molecular structures, as well as their notable complexity, constitutes an ideal
context in which DL-based approaches can be profitably employed. To express the
full potential of these techniques, though, it is a prerequisite to express the
information contained in the molecule's atomic positions and distances in a set
of input quantities that the network can process. Many of the molecular
descriptors devised insofar are effective and manageable for relatively small
structures, but become complex and cumbersome for larger ones. Furthermore,
most of them are defined locally, a feature that could represent a limit for
those applications where global properties are of interest. Here, we build a
deep learning architecture capable of predicting non-trivial and intrinsically
global quantities, that is, the eigenvalues of a protein's lowest-energy
fluctuation modes. This application represents a first, relatively simple test
bed for the development of a neural network approach to the quantitative
analysis of protein structures, and demonstrates unexpected use in the
identification of mechanically relevant regions of the molecule.
