Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2024
Abstract
Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew’s correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes.
Discipline
Artificial Intelligence and Robotics | Bioinformatics | Databases and Information Systems
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
Bioinformatics
Volume
40
Issue
7
First Page
1
Last Page
12
ISSN
1367-4803
Identifier
10.1093/bioinformatics/btae378
Publisher
Oxford University Press
Citation
ZHENG, Dandan; ZHOU, Siyu; CHEN, Lihong; PANG, Guansong; and YANG, Jian.
A deep learning method to predict bacterial ADP-ribosyltransferase toxins. (2024). Bioinformatics. 40, (7), 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/9033
Copyright Owner and License
Authors-CC-BY
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1093/bioinformatics/btae378
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Databases and Information Systems Commons