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

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

Pedro Hermosilla Ulm University Marco Schäfer University of Tübingen Matej Lang Masaryk University (Brno) Gloria Fackelmann Ulm University Pere-Pau Vázquez Universitat Politècnica de Catalunya Barbora Kozlíková Masaryk University (Brno) Michael Krone University of Tübingen Tobias Ritschel University College London Timo Ropinski Ulm University

International Conference on Learning Representations, 2021


Proteins perform a large variety of functions in living organisms and thus play a key role in biology. However, commonly used algorithms in protein 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.


	title={Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures},
	author={Hermosilla, Pedro and Sch{\"a}fer, Marco and Lang, Matej and Fackelmann, Gloria and V{\'a}zquez, Pere-Pau and Kozl{\'i}kov{\'a}, Barbora and Krone, Michael and Ritschel, Tobias and Ropinski, Timo},
	bookTitle={Proceedings of International Conference on Learning Representations}