Work

GTT-EC: a hierarchical graph transformer framework for enzyme function prediction via functional residue modeling

Year: 2026

Type: article

Abstract: Current enzyme commission (EC) number prediction methods often rely on functional residue annotations and lack robustness. Here, we propose GTT-EC, a hierarchical graph transformer framework for EC number prediction. Without requiring explicit residue annotations, GTT-EC constructs protein graphs to integrate sequential and structural features to learn discriminative representations. Furthermore, it utilizes attention mechanisms to capture long-range interactions and dynamically focus on key functional regions. Experiments demonstrate that GTT-EC outperforms state-of-the-art methods in both accuracy and robustness across varying sequence similarities. Importantly, it effectively identifies functional regions without prior site inputs, demonstrating broad potential for downstream applications.

Source: Genome biology

DOI: 10.1186/s13059-026-04232-6

Authors: Zihan Luo, Yuhang Liu, Xiangrong Liu, Leyi Wei

Institutions: Xiamen University, Macao Polytechnic University

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funder: National Natural Science Foundation of China