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

Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlikova, Michael Krone, Tobias Ritschel, Timo Ropinski

Keywords: classification, bioinformatics

Abstract: Proteins perform a large variety of functions in living organisms and thus play a key role in biology. However, commonly used algorithms in protein representation learning were not specifically designed for protein data, and are therefore not able to capture all relevant structural levels of a protein during learning. To fill this gap, we propose two new learning operators, specifically designed to process protein structures. First, we introduce a novel convolution operator that considers the primary, secondary, and tertiary structure of a protein by using $n$-D convolutions defined on both the Euclidean distance, as well as multiple geodesic distances between the atoms in a multi-graph. Second, we introduce a set of hierarchical pooling operators that enable multi-scale protein analysis. We further evaluate the accuracy of our algorithms on common downstream tasks, where we outperform state-of-the-art protein learning algorithms.

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One-sentence Summary: We present a new neural network architecture to process 3D protein structures.

Code: [ phermosilla/IEConv_proteins](https://github.com/phermosilla/IEConv_proteins)

Data: [PROTEINS](https://paperswithcode.com/dataset/proteins)