  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.

Quantitative Methods;Machine Learning