  Predicting the structure of a protein from its sequence is a cornerstone task
of molecular biology. Established methods in the field, such as homology
modeling and fragment assembly, appeared to have reached their limit. However,
this year saw the emergence of promising new approaches: end-to-end protein
structure and dynamics models, as well as reinforcement learning applied to
protein folding. For these approaches to be investigated on a larger scale, an
efficient implementation of their key computational primitives is required. In
this paper we present a library of differentiable mappings from two standard
dihedral-angle representations of protein structure (full-atom representation
"$\phi,\psi,\omega,\chi$" and backbone-only representation
"$\phi,\psi,\omega$") to atomic Cartesian coordinates. The source code and
documentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.
