Graph Neural Network-Based Approaches for Protein Function Prediction

Protein functions often involve a dynamic interplay that covers a variety of molecular interactions that can be represented and analyzed in a 3-dimensional space. To this end, researchers have applied graph neural networks (GNNs) that effectively model such spaces as a promising methodology to predict protein functions. We discuss the graph-based representations of proteins that are applied to different prediction tasks, which include graphs at various levels of granularity: atomic, residue, and multi-scale. We also review various protein function prediction tools that rely on GNN architectures that learn representations from protein graphs, specifically in the context of the Gene Ontology prediction and protein-protein interaction prediction. GNN-based methods leverage the underlying structural knowledge and offer a promising future in improving the quality of the protein function predictions.