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Hu Y, Wang Y, Hu X, Chao H, Li S, Ni Q, Zhu Y, Hu Y, Zhao Z, Chen M. T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors. Comput Struct Biotechnol J 2024; 23:801-812. [PMID: 38328004 PMCID: PMC10847861 DOI: 10.1016/j.csbj.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024] Open
Abstract
Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.
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Affiliation(s)
- Yueming Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Yejun Wang
- Youth Innovation Team of Medical Bioinformatics, Shenzhen University Medical School, Shenzhen, China
- Department of Cell Biology and Genetics, College of Basic Medicine, Shenzhen University Medical School, Shenzhen, China
| | - Xiaotian Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Sida Li
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Qinyang Ni
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Yanyan Zhu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Yixue Hu
- Youth Innovation Team of Medical Bioinformatics, Shenzhen University Medical School, Shenzhen, China
| | - Ziyi Zhao
- Youth Innovation Team of Medical Bioinformatics, Shenzhen University Medical School, Shenzhen, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
- Institute of Hematology, Zhejiang University School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou 310058, China
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