Before revision:  We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction .
After revision:  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 method for protein interface prediction .