GLProtein: Global-and-Local Structure Aware Protein Representation Learning
This work addresses the need for better protein function prediction in bioinformatics, offering a novel framework that combines structural details, though it may be incremental in its approach.
The paper tackles the problem of integrating global and local structural information in protein representation learning, proposing GLProtein, which outperforms previous methods in tasks like protein-protein interaction and contact prediction.
Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein sequence analysis, there remains potential for further exploration in integrating protein structural information. We argue that the structural information of proteins is not only limited to their 3D information but also encompasses information from amino acid molecules (local information) to protein-protein structure similarity (global information). To address this, we propose \textbf{GLProtein}, the first framework in protein pre-training that incorporates both global structural similarity and local amino acid details to enhance prediction accuracy and functional insights. GLProtein innovatively combines protein-masked modelling with triplet structure similarity scoring, protein 3D distance encoding and substructure-based amino acid molecule encoding. Experimental results demonstrate that GLProtein outperforms previous methods in several bioinformatics tasks, including predicting protein-protein interaction, contact prediction, and so on.