Graph Neural Network-Based Approaches for Protein Function Prediction
Document Type
Article
Publication Date
7-30-2025
Department
College of Computing
Abstract
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.
Publication Title
Methods in Molecular Biology Clifton N J
Recommended Citation
Chaudhari, M.,
Bahmani, S.,
Pratyush, P.,
Garrett, S.,
Thapa, N.,
&
Kc, D.
(2025).
Graph Neural Network-Based Approaches for Protein Function Prediction.
Methods in Molecular Biology Clifton N J,
2947, 49-74.
http://doi.org/10.1007/978-1-0716-4662-5_3
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/1967