  Proteins are miniature machines whose function depends on their
three-dimensional (3D) structure. Determining this structure computationally
remains an unsolved grand challenge. A major bottleneck involves selecting the
most accurate structural model among a large pool of candidates, a task
addressed in model quality assessment. Here, we present a novel deep learning
approach to assess the quality of a protein model. Our network builds on a
point-based representation of the atomic structure and rotation-equivariant
convolutions at different levels of structural resolution. These combined
aspects allow the network to learn end-to-end from entire protein structures.
Our method achieves state-of-the-art results in scoring protein models
submitted to recent rounds of CASP, a blind prediction community experiment.
Particularly striking is that our method does not use physics-inspired energy
terms and does not rely on the availability of additional information (beyond
the atomic structure of the individual protein model), such as sequence
alignments of multiple proteins.
