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Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

commonly used algorithms in protein learningwere specifically designed for protein data, and are able tocapture all relevant structural levels of a protein during learning. To fill this gap,we propose two new learning operators, specifically designed to process proteinstructures. First, we introduce a novel convolution operator that considers theprimary, secondary, and tertiary structure of a protein by usingn-D convolutionsdefined on both the Euclidean distance, as well as multiple geodesic distancesbetween the atoms in a multi-graph. Second, we introduce a set of hierarchicalpooling operators that enable multi-scale protein analysis. We further evaluate theaccuracy of our algorithms on common downstream tasks, where we outperformstate-of-the-art protein learning algorithms.