Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.

<h4>Background</h4>As 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-...

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Autores principales:	Xiaobao Dong, Yong-Jun Zhang, Ziding Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ab4a91e91c74043b943d570766b4c2e
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spelling	oai:doaj.org-article:2ab4a91e91c74043b943d570766b4c2e2021-11-18T07:56:50ZUsing weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.1932-620310.1371/journal.pone.0056632https://doaj.org/article/2ab4a91e91c74043b943d570766b4c2e2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23437191/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>As 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.<h4>Results</h4>For 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/.Xiaobao DongYong-Jun ZhangZiding ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e56632 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaobao Dong Yong-Jun Zhang Ziding Zhang Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
description	<h4>Background</h4>As 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.<h4>Results</h4>For 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/.
format	article
author	Xiaobao Dong Yong-Jun Zhang Ziding Zhang
author_facet	Xiaobao Dong Yong-Jun Zhang Ziding Zhang
author_sort	Xiaobao Dong
title	Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
title_short	Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
title_full	Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
title_fullStr	Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
title_full_unstemmed	Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
title_sort	using weakly conserved motifs hidden in secretion signals to identify type-iii effectors from bacterial pathogen genomes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/2ab4a91e91c74043b943d570766b4c2e
work_keys_str_mv	AT xiaobaodong usingweaklyconservedmotifshiddeninsecretionsignalstoidentifytypeiiieffectorsfrombacterialpathogengenomes AT yongjunzhang usingweaklyconservedmotifshiddeninsecretionsignalstoidentifytypeiiieffectorsfrombacterialpathogengenomes AT zidingzhang usingweaklyconservedmotifshiddeninsecretionsignalstoidentifytypeiiieffectorsfrombacterialpathogengenomes
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Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.

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