ArticlePloS one2013
Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Natural language processing approach to model the secretion signal of type III effectors.Frontiers in plant science · 2022Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.Frontiers in microbiology · 2021Article
- High-Level Production of Bacteriotoxic Phospholipase A1 in Bacterial Host Pseudomonas fluorescens Via ABC Transporter-Mediated Secretion and Inducible Expression.Microorganisms · 2020Article
- ACNNT3: Attention-CNN Framework for Prediction of Sequence-Based Bacterial Type III Secreted Effectors.Computational and mathematical methods in medicine · 2020Article
- Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information.Scientific reports · 2019Article
- Bastion3: a two-layer ensemble predictor of type III secreted effectors.Bioinformatics (Oxford, England) · 2019Article
- Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework.Scientific reports · 2018Article
- NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features.Molecules (Basel, Switzerland) · 2018Article
- GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features.PloS one · 2018Article
- SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems.Scientific reports · 2017Article
- A systematic identification of species-specific protein succinylation sites using joint element features information.International journal of nanomedicine · 2017Article
- Type III-Dependent Translocation of HrpB2 by a Nonpathogenic hpaABC Mutant of the Plant-Pathogenic Bacterium Xanthomonas campestris pv. vesicatoria.Applied and environmental microbiology · 2016Article
- Computational Prediction of Effector Proteins in Fungi: Opportunities and Challenges.Frontiers in plant science · 2016Review
- BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.Database : the journal of biological databases and curation · 2015Article
- Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.PloS one · 2015Article
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3 authors.
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Abstract
backgroundAs one of the most important virulence factor types in gram-negative pathogenic bacteria, type-III effectors (TTEs) play a crucial role in pathogen-host interactions by directly influencing immune signaling pathways within host cells. Based on the hypothesis that type-III secretion signals may be comprised of some weakly conserved sequence motifs, here we used profile-based amino acid pair information to develop an accurate TTE predictor.
resultsFor a TTE or non-TTE, we first used a hidden Markov model-based sequence searching method (i.e., HHblits) to detect its weakly homologous sequences and extracted the profile-based k-spaced amino acid pair composition (HH-CKSAAP) from the N-terminal sequences. In the next step, the feature vector HH-CKSAAP was used to train a linear support vector machine model, which we designate as BEAN (Bacterial Effector ANalyzer). We compared our method with four existing TTE predictors through an independent test set, and our method revealed improved performance. Furthermore, we listed the most predictive amino acid pairs according to their weights in the established classification model. Evolutionary analysis shows that predictive amino acid pairs tend to be more conserved. Some predictive amino acid pairs also show significantly different position distributions between TTEs and non-TTEs. These analyses confirmed that some weakly conserved sequence motifs may play important roles in type-III secretion signals. Finally, we also used BEAN to scan one plant pathogen genome and showed that BEAN can be used for genome-wide TTE identification. The webserver and stand-alone version of BEAN are available at http://protein.cau.edu.cn:8080/bean/.
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