Geometric Graph Representation Learning on Protein Structure Prediction

Abstract

Determining a protein's 3D from its sequences is one of the most challenging problems in biology. Recently, geometric deep learning has achieved great success on non-Euclidean domains including social networks, chemistry, and computer graphics. Although it is natural to present protein structures as 3D graphs, existing research has rarely studied protein structures as graphs directly. The present research explores the geometry deep learning of three-dimensional graphs on protein structures and proposes a graph neural network architecture to address these challenges. The proposed Protein Geometric Graph Neural Network (PG-GNN) models both distance geometric graph representation and dihedral geometric graph representation by geometric graph convolutions. This research shed new light on protein 3D structure studies. We investigated the effectiveness of graph neural networks over five real datasets. Our results demonstrate the potential of GNNs for 3D structure prediction.