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T1SEstacker: A tri-layer stacking model effectively predicts bacterial type 1 secreted proteins based on C-terminal non-RTX-motif sequence features

Chen, Z.; Zhao, Z.; Hui, X.; Zhang, J.; Hu, Y.; Chen, R.; Hu, Y.; Wang, Y.

2021-11-12 microbiology
10.1101/2021.11.10.468166 bioRxiv
Show abstract

The proteins secreted through type 1 secretion systems often play important roles in pathogenicity of various gram-negative bacteria. However, the type 1 secretion mechanism remains unknown. In this research, we observed the sequence features of RTX proteins, a major class of type 1 secreted substrates. We found striking non-RTX-motif amino acid composition patterns at the C-termini, most typically exemplified by the enriched [FLI][VAI] at the most C-terminal two positions. Machine-learning models, including deep-learning models, were trained using these sequence-based non-RTX-motif features, and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a 5-fold cross-validated sensitivity of ~0.89 at the specificity of ~0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif type 1 secreted proteins, further suggesting their potential existence of common secretion signals. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C-termini, and developed a stacking model that can predict type 1 secreted proteins accurately.

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