Title: Transferrable End-to-End Learning for Protein Interface Prediction,
Abstract: While there has been an explosion in the number of experimentally determined,
atomically detailed structures of proteins, how to represent these structures
in a machine learning context remains an open research question. In this work
we demonstrate that representations learned from raw atomic coordinates can
outperform hand-engineered structural features while displaying a much higher
degree of transferrability. To do so, we focus on a central problem in biology:
predicting how proteins interact with one another--that is, which surfaces of
one protein bind to which surfaces of another protein. We present Siamese
Atomic Surfacelet Network (SASNet), the first end-to-end learning method for
protein interface prediction. Despite using only spatial coordinates and
identities of atoms as inputs, SASNet outperforms state-of-the-art methods that
rely on hand-engineered, high-level features. These results are particularly
striking because we train the method entirely on a significantly biased data
set that does not account for the fact that proteins deform when binding to one
another. Demonstrating the first successful application of transfer learning to
atomic-level data, our network maintains high performance, without retraining,
when tested on real cases in which proteins do deform.