Before revision: 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.
After revision: 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 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 which surfaces of another protein.