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Predicting the structure of a protein from its sequence is a cornerstone taskof molecular biology. Established methods in the field, such as homologymodeling and fragment assembly, appeared to have reached their limit. However,this year saw the emergence of promising new approaches: end-to-end proteinstructure and dynamics models, as well as reinforcement learning applied toprotein folding. For these approaches to be investigated on a larger scale, anefficient implementation of their key computational primitives is required. Inthis paper we present a library of differentiable mappings from two standarddihedral-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 anddocumentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.

TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure