article · IEEE Transactions on Computational Biology and Bioinformatics · 2025
SEFP: Structure-Based Enzyme Function Prediction
Ziqi Zhang, Guan Yu Yu, Zhaohong Deng, Chuping Luo, Cheng Cai, Wei Zhang, Fuping Hu, Kup‐Sze Choi, Zhisheng Wei, Lei Wang, Jing Wu
Cited by 1Abstract and details only
Abstract
Traditional biological experimental methods to determine enzyme properties are time-consuming and costly, leading to an increasing interest in computational models for enzyme function prediction. However, the existing computational methods are insufficient and inefficient to exploit enzyme structure. In this work, we introduce SEFP, a novel method leveraging enzyme point clouds for enzyme function prediction. The structure encoder of SEFP uses a tailored enzyme point cloud network to analyze the three-dimensional arrangement of atoms within the enzyme, integrating hierarchical residue global features through a residue feature adapter to extract detailed enzyme point features. Additionally, the Bio-BCS residue feature encoder extracts enzyme residue features with channel and spatial weights using a specially designed attention mechanism. Finally, SEFP fuses point and residue features to generate the final prediction results. Comparative evaluations show that SEFP outperforms various recent computational methods, demonstrating superior performance. On the RSCB enzyme structure dataset, SEFP achieves an f1-score of 95.85, outperforming two representative structure-based methods, EnzyNet and DeepFri. On the HECNet dataset, SEFP maintains its superiority over all comparison sequence-based methods, yielding an f1-score of 94.29. Ablation studies are conducted to confirm the effectiveness of individual modules within SEFP. These findings underscore the potential of SEFP for reliable and precise enzyme function prediction, offering advancements in bioinformatics and computational biology.
Cite this paper
Zhang, Z., Yu, G. Y., Deng, Z., Luo, C., Cai, C., Zhang, W., Hu, F., Choi, K., Wei, Z., Wang, L., & Wu, J. (2025). SEFP: Structure-Based Enzyme Function Prediction. IEEE Transactions on Computational Biology and Bioinformatics, 22(3), 1211–1221. https://doi.org/10.1109/tcbbio.2025.3552846
Zhang, Ziqi, et al. “SEFP: Structure-Based Enzyme Function Prediction.” IEEE Transactions on Computational Biology and Bioinformatics, vol. 22, no. 3, 2025, pp. 1211–21, https://doi.org/10.1109/tcbbio.2025.3552846.
Zhang, Ziqi, Guan Yu Yu, Zhaohong Deng, Chuping Luo, Cheng Cai, Wei Zhang, Fuping Hu, et al. 2025. “SEFP: Structure-Based Enzyme Function Prediction.” IEEE Transactions on Computational Biology and Bioinformatics 22 (3): 1211–21. https://doi.org/10.1109/tcbbio.2025.3552846.
Zhang, Z. et al. (2025) ‘SEFP: Structure-Based Enzyme Function Prediction’, IEEE Transactions on Computational Biology and Bioinformatics, 22(3), pp. 1211–1221. Available at: https://doi.org/10.1109/tcbbio.2025.3552846.
@article{zhang2025sefp,
title = {{SEFP: Structure-Based Enzyme Function Prediction}},
author = {Zhang, Ziqi and Yu, Guan Yu and Deng, Zhaohong and Luo, Chuping and Cai, Cheng and Zhang, Wei and Hu, Fuping and Choi, Kup‐Sze and Wei, Zhisheng and Wang, Lei and Wu, Jing},
journal = {IEEE Transactions on Computational Biology and Bioinformatics},
volume = {22},
number = {3},
pages = {1211--1221},
year = {2025},
doi = {10.1109/tcbbio.2025.3552846}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.