Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology. Here 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 complex, hand-selected 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. Nonetheless , our network maintains high performance, without retraining, when tested on real cases in which proteins do deform . This suggests that it has learned fundamental properties of protein structure and dynamics, which has important implications for a variety of key problems related to biomolecular structure .
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 .
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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
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