ArticleComputational and structural biotechnology journal2024
T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics.PLoS computational biology · 2026Article
- Article
- Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species.BMC microbiology · 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
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- TXSelect: A multi-task learning model to identify secretory effectors.PLoS computational biology · 2025Article
- Comparative single-cell genomics of two uncultivatedmSphere · 2025Article
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- Coxiella burnetii as a model system for understanding host immune response against obligate intracellular, vacuolar pathogens.PLoS pathogens · 2025Article
- Article
- Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.Nature communications · 2025Article
- Language models can identify enzymatic binding sites in protein sequences.Computational and structural biotechnology journal · 2024Article
- T4Seeker: a hybrid model for type IV secretion effectors identification.BMC biology · 2024Article
- The F plasmid conjutome: the repertoire ofmSphere · 2024Article
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Authors and funding
10 authors.
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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
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