T4SEpp: a pipeline integrated with protein language models effectively predicting bacterial type IV secreted effectors

Hu, Y.; Wang, Y.; Hu, X.; Chao, H.; Li, S.; Ni, Q.; Zhu, Y.; Zhao, Z.; Chen, M.

doi:10.1101/2023.07.01.547179

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bioRxiv · 10.1101/2023.07.01.547179

T4SEpp: a pipeline integrated with protein language models effectively predicting bacterial type IV secreted effectors

Hu, Y.·Wang, Y.·Hu, X.·Chao, H.·Li, S.·Ni, Q.·Zhu, Y.·Zhao, Z.·Chen, M.

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Abstract

Many pathogenic bacteria use type IV secretion systems(T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causeing 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. An integrated model T4SEpp was assembled by a module searching full-length, signal sequence and effector domain homologs of known T4SEs, a machine learning module based on the hand-crafted features extracted from the signal sequences, and the third module containing three best-performing protein language pre-trained models. T4SEpp outperformed the other state-of-the-art (SOTA) software tools, achieving [~]0.95 sensitivity at a high specificity of [~]0.99, based on the assessment of an independent testing dataset. Additionally, we performed a comprehensive search among 8,761 bacterial species, leading to the discovery of 227 species belonging to 3 phyla and 117 genera that possess T4SSs. Furthermore, leveraging the power of T4SEpp, we successfully identified a grand total of 12,622 plausible T4SEs. 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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Hu, Y., Wang, Y., Hu, X., Chao, H., Li, S., Ni, Q., Zhu, Y., Zhao, Z., Chen, M.. 2023-07-03. T4SEpp: a pipeline integrated with protein language models effectively predicting bacterial type IV secreted effectors. https://doi.org/10.1101/2023.07.01.547179

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