Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning

• URL: http://arxiv.org/abs/2201.13299v5
• Date: Tue, 26 Nov 2024 15:18:30 GMT
• Title: Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
• Authors: Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, Jianzhu Ma,
• Abstract summary: We propose the Orientation-Aware Graph Neural Networks (OAGNNs) to better sense the geometric characteristics in protein structure. Extending a single weight from a scalar to a 3D vector, we construct a rich set of geometric-meaningful operations. OAGNNs have a remarkable ability to sense geometric orientational features compared to classical networks.
• Score: 29.366321002562373
• License:
• Abstract: By folding to particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the global backbone topology but the local fine-grained orientational relations between amino acids should also be considered. In this work, we propose the Orientation-Aware Graph Neural Networks (OAGNNs) to better sense the geometric characteristics in protein structure (e.g. inner-residue torsion angles, inter-residue orientations). Extending a single weight from a scalar to a 3D vector, we construct a rich set of geometric-meaningful operations to process both the classical and SO(3) representations of a given structure. To plug our designed perceptron unit into existing Graph Neural Networks, we further introduce an equivariant message passing paradigm, showing superior versatility in maintaining SO(3)-equivariance at the global scale. Experiments have shown that our OAGNNs have a remarkable ability to sense geometric orientational features compared to classical networks. OAGNNs have also achieved state-of-the-art performance on various computational biology applications related to protein 3D structures.

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