  Despite an explosion in the number of experimentally determined, atomically
detailed structures of biomolecules, many critical tasks in structural biology
remain data-limited. Whether performance in such tasks can be improved by using
large repositories of tangentially related structural data remains an open
question. To address this question, we focused on a central problem in biology:
predicting how proteins interact with one another---that is, which surfaces of
one protein bind to those of another protein. We built a training dataset, the
Database of Interacting Protein Structures (DIPS), that contains biases but is
two orders of magnitude larger than those used previously. We found that these
biases significantly degrade the performance of existing methods on
gold-standard data. Hypothesizing that assumptions baked into the hand-crafted
features on which these methods depend were the source of the problem, we
developed the first end-to-end learning model for protein interface prediction,
the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates
and identities of atoms, SASNet outperforms state-of-the-art methods trained on
gold-standard structural data, even when trained on only 3% of our new dataset.
Code and data available at https://github.com/drorlab/DIPS.
