Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features Article Swipe
Yueming Hu
,
Maodong Yan
,
Yanyan Zhu
,
Haoyu Chao
,
Sida Li
,
Qinyang Ni
,
Yanshi Hu
,
Enyan Liu
,
Liya Liu
,
Yifan Chen
,
Zheng‐Yi Zhou
,
Yuhao Chen
,
Shilong Zhang
,
Yejun Wang
,
Cong Feng
,
Ming Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.07.642067
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.07.642067
Type VI secretion system effectors (T6SEs) are crucial for bacterial pathogenicity, making their accurate identification essential for understanding bacterial virulence mechanisms. This study analyzed the differences in amino acid composition of N-terminal signal sequences between T6SEs and non-T6SEs, uncovering distinct positional amino acid preferences in T6SEs. Using a combination of unsupervised and supervised analysis, we evaluated feature encoding methods and developed T6CNN, an ensemble model that integrates N-terminal signal sequences, evolutionary information, and pre-trained protein language features for T6SE prediction. T6CNN demonstrated outstanding performance in independent testing, outperforming existing tools with a 7.9% accuracy increase (to 0.953), a 13.2% sensitivity improvement (to 0.964), and a 6.6% specificity enhancement (to 0.951). The T6CNN model offers a reliable and accurate solution for T6SE prediction, with significant potential to advance research on bacterial pathogenicity. Importance This study introduces T6CNN, a new computational model for identifying Type VI secretion system effectors used by harmful bacteria. By analyzing early protein sequences, T6CNN uncovers unique features that reliably distinguish effector proteins. Integrating evolutionary data and pre-trained protein language features, the model outperforms existing methods in accuracy, sensitivity, and specificity. This enhanced prediction tool deepens our understanding of bacterial infection mechanisms and offers researchers a valuable resource for pinpointing key virulence factors. Ultimately, T6CNN may help drive the development of more targeted antibacterial treatments and strategies to combat bacterial diseases.
Related Topics
Concepts
Effector
Convolutional neural network
Terminal (telecommunication)
SIGNAL (programming language)
Signal peptide
Computer science
Secretion
Artificial neural network
Type (biology)
Artificial intelligence
Computational biology
Cell biology
Biology
Peptide sequence
Computer network
Genetics
Programming language
Biochemistry
Gene
Ecology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.07.642067
- https://www.biorxiv.org/content/biorxiv/early/2025/03/12/2025.03.07.642067.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408412676
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408412676Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.03.07.642067Digital Object Identifier
- Title
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Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language FeaturesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-12Full publication date if available
- Authors
-
Yueming Hu, Maodong Yan, Yanyan Zhu, Haoyu Chao, Sida Li, Qinyang Ni, Yanshi Hu, Enyan Liu, Liya Liu, Yifan Chen, Zheng‐Yi Zhou, Yuhao Chen, Shilong Zhang, Yejun Wang, Cong Feng, Ming ChenList of authors in order
- Landing page
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https://doi.org/10.1101/2025.03.07.642067Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/03/12/2025.03.07.642067.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/03/12/2025.03.07.642067.full.pdfDirect OA link when available
- Concepts
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Effector, Convolutional neural network, Terminal (telecommunication), SIGNAL (programming language), Signal peptide, Computer science, Secretion, Artificial neural network, Type (biology), Artificial intelligence, Computational biology, Cell biology, Biology, Peptide sequence, Computer network, Genetics, Programming language, Biochemistry, Gene, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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