  Predicting the structure of multi-protein complexes is a grand challenge in
biochemistry, with major implications for basic science and drug discovery.
Computational structure prediction methods generally leverage pre-defined
structural features to distinguish accurate structural models from less
accurate ones. This raises the question of whether it is possible to learn
characteristics of accurate models directly from atomic coordinates of protein
complexes, with no prior assumptions. Here we introduce a machine learning
method that learns directly from the 3D positions of all atoms to identify
accurate models of protein complexes, without using any pre-computed
physics-inspired or statistical terms. Our neural network architecture combines
multiple ingredients that together enable end-to-end learning from molecular
structures containing tens of thousands of atoms: a point-based representation
of atoms, equivariance with respect to rotation and translation, local
convolutions, and hierarchical subsampling operations. When used in combination
with previously developed scoring functions, our network substantially improves
the identification of accurate structural models among a large set of possible
models. Our network can also be used to predict the accuracy of a given
structural model in absolute terms. The architecture we present is readily
applicable to other tasks involving learning on 3D structures of large atomic
systems.
