ArticleFrontiers in microbiology2021
T1SEstacker: A Tri-Layer Stacking Model Effectively Predicts Bacterial Type 1 Secreted Proteins Based on C-Terminal Non-repeats-in-Toxin-Motif Sequence Features.
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 9 citations in OpenAlex.
- GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics.PLoS computational biology · 2026Article
- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
- MoCETSE: A mixture-of-convolutional experts and transformer-based model for predicting Gram-negative bacterial secreted effectors.PLoS computational biology · 2026Article
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- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Comprehensive Genomic Analysis Reveals Extensive Diversity of Type I and Type IV Secretion Systems in Klebsiella pneumoniae.Current microbiology · 2023Article
- DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria.Research (Washington, D.C.) · 2023Article
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9 authors at 3 institutions in 1 country.
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Abstract
Type 1 secretion systems play important roles in pathogenicity of Gram-negative bacteria. However, the substrate secretion mechanism remains largely unknown. In this research, we observed the sequence features of repeats-in-toxin (RTX) proteins, a major class of type 1 secreted effectors (T1SEs). 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 ones, 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 fivefold 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 T1SEs, further suggesting their potential existence of common secretion signals. T1SEstacker was applied to predict T1SEs from the genomes of representative
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