High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles

Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limi...

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Veröffentlicht in:	Bioinformatics Jg. 27; H. 6; S. 777 - 784
Hauptverfasser:	Wang, Yejun, Zhang, Qing, Sun, Ming-an, Guo, Dianjing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Oxford University Press 15.03.2011
Schlagworte:	Algorithms Amino Acid Sequence Amino acids Bacteria Bacterial Proteins - chemistry Bacterial Proteins - classification Bacterial Secretion Systems Bayes Theorem Bioinformatics Biological and medical sciences Composition effects Computational Biology - methods Effectors Fundamental and applied biological sciences. Psychology General aspects Genome, Bacterial Mathematical models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Statistical Proteins Ralstonia solanacearum Ralstonia solanacearum - chemistry Secretions Sequence Analysis, Protein - methods Software Support vector machines Composition Accuracy Position Secretion Aminoacid Prediction Bacteria Profile
ISSN:	1367-4803, 1367-4811, 1367-4811, 1460-2059
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Abstract	Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. Results: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. Availability: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac. Contact: djguo@cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.
AbstractList	Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac. Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified.Results: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion ( similar to 50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of similar to 90.97% and an average selectivity of similar to 97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model.Availability: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac.Contact:[ /bold] djguo[at]cuhk.edu.hkSupplementary information: Supplementary data are available at Bioinformatics online. Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. Results: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. Availability: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac. Contact: djguo@cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online. Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified.MOTIVATIONBacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified.In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model.RESULTSIn this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model.An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac.AVAILABILITYAn R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac.
Author	Wang, Yejun Sun, Ming-an Zhang, Qing Guo, Dianjing
Author_xml	– sequence: 1 givenname: Yejun surname: Wang fullname: Wang, Yejun – sequence: 2 givenname: Qing surname: Zhang fullname: Zhang, Qing – sequence: 3 givenname: Ming-an surname: Sun fullname: Sun, Ming-an – sequence: 4 givenname: Dianjing surname: Guo fullname: Guo, Dianjing
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Cites_doi	10.1016/j.canlet.2007.12.022 10.1093/bioinformatics/16.4.404 10.1038/nature05272 10.1371/journal.ppat.1000375 10.1128/JB.00180-06 10.1038/415497a 10.1073/pnas.112183899 10.1128/mr.57.4.995-1017.1993 10.1038/35102073 10.1093/bioinformatics/bth382 10.1128/JB.187.5.1559-1567.2005 10.1046/j.1365-2958.2003.03772.x 10.1073/pnas.230450797 10.1093/nar/gki396 10.1186/1471-2105-11-S1-S47 10.1371/journal.pone.0004920 10.1371/journal.pone.0005917 10.1078/1438-4221-00199 10.1046/j.1365-2958.2002.03196.x 10.1128/JB.186.2.543-555.2004 10.1101/gr.849004 10.1371/journal.ppat.1000376 10.1046/j.1365-2958.2002.02738.x 10.1073/pnas.0604891103 10.1128/MMBR.68.4.692-744.2004 10.1111/j.1365-2958.2005.04823.x 10.1046/j.1365-2958.2001.02271.x 10.1093/bioinformatics/14.9.755 10.1094/MPMI-23-3-0251 10.1016/j.mib.2008.01.006 10.1128/MMBR.62.2.379-433.1998
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References	Fath (2023012511540714100_B6) 1993; 57 Lloyd (2023012511540714100_B13) 2001; 39 Hueck (2023012511540714100_B10) 1998; 62 Wang (2023012511540714100_B31) 2008; 263 Kim (2023012511540714100_B12) 2004; 20 Panina (2023012511540714100_B20) 2005; 58 Tobe (2023012511540714100_B30) 2006; 103 Cheng (2023012511540714100_B3) 2005; 33 Petnicki-Ocwieja (2023012511540714100_B21) 2002; 99 Mukaihara (2023012511540714100_B19) 2010; 23 Bingle (2023012511540714100_B2) 2008; 11 Meyer (2023012511540714100_B17) 2006; 188 Ramamurthi (2023012511540714100_B22) 2003; 50 McGuffin (2023012511540714100_B16) 2000; 16 Samudrala (2023012511540714100_B25) 2009; 5 Galán (2023012511540714100_B8) 2006; 444 Löwer (2023012511540714100_B15) 2009; 4 Lloyd (2023012511540714100_B14) 2002; 43 Rüssmann (2023012511540714100_B23) 2002; 46 Arnold (2023012511540714100_B1) 2009; 5 Shao (2023012511540714100_B28) 2009; 4 Henderson (2023012511540714100_B9) 2004; 68 Fischer (2023012511540714100_B7) 2002; 292 Mudgett (2023012511540714100_B18) 2000; 97 Eddy (2023012511540714100_B5) 1998; 14 Karavolos (2023012511540714100_B11) 2005; 187 Crooks (2023012511540714100_B4) 2004; 14 Yang (2023012511540714100_B32) 2010; 11 Stebbins (2023012511540714100_B29) 2001; 414 Salanoubat (2023012511540714100_B24) 2002; 415 Scholkopf (2023012511540714100_B26) 2002 Schechter (2023012511540714100_B27) 2004; 186
References_xml	– volume: 263 start-page: 67 year: 2008 ident: 2023012511540714100_B31 article-title: Two oral HBx vaccines delivered by live attenuated Salmonella: both eliciting effective anti-tumor immunity publication-title: Cancer Lett. doi: 10.1016/j.canlet.2007.12.022 – volume: 16 start-page: 404 year: 2000 ident: 2023012511540714100_B16 article-title: The PSIPRED protein structure prediction server publication-title: Bioinformatics doi: 10.1093/bioinformatics/16.4.404 – volume: 444 start-page: 567 year: 2006 ident: 2023012511540714100_B8 article-title: Protein delivery into eukaryotic cells by type III secretion machines publication-title: Nature doi: 10.1038/nature05272 – volume: 5 start-page: e1000375 year: 2009 ident: 2023012511540714100_B25 article-title: Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems publication-title: PLoS pathogens doi: 10.1371/journal.ppat.1000375 – volume: 188 start-page: 4903 year: 2006 ident: 2023012511540714100_B17 article-title: PopF1 and PopF2, two proteins secreted by the Type III protein secretion system of Ralstonia solanacearum are translocators belonging to the HrpF/NopX family publication-title: J. Bacteriol. doi: 10.1128/JB.00180-06 – volume: 415 start-page: 497 year: 2002 ident: 2023012511540714100_B24 article-title: Genome sequence of the plant pathogen Ralstonia solanacearum publication-title: Nature doi: 10.1038/415497a – volume: 99 start-page: 7652 year: 2002 ident: 2023012511540714100_B21 article-title: Genomewide identification of proteins secreted by the Hrp type III protein secretion system of Pseudomonas syringae pv. tomato DC3000 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.112183899 – volume: 57 start-page: 995 year: 1993 ident: 2023012511540714100_B6 article-title: ABC transporters: bacterial exporters publication-title: Microbiol. Rev. doi: 10.1128/mr.57.4.995-1017.1993 – volume: 414 start-page: 77 year: 2001 ident: 2023012511540714100_B29 article-title: Maintenance of an unfolded polypeptide by a cognate chaperone in bacterial type III secretion publication-title: Nature doi: 10.1038/35102073 – volume: 20 start-page: 3179 year: 2004 ident: 2023012511540714100_B12 article-title: Prediction of phosphorylation sites using SVMs publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth382 – volume: 187 start-page: 1559 year: 2005 ident: 2023012511540714100_B11 article-title: Type III secretion of the Salmonella effector protein SopE is mediated via an N-terminal amino acid signal and not an mRNA sequence publication-title: J. Bacteriol. doi: 10.1128/JB.187.5.1559-1567.2005 – volume: 50 start-page: 1189 year: 2003 ident: 2023012511540714100_B22 article-title: Yersinia yopQ mRNA encodes a bipartite type III secretion signal in the first 15 codons publication-title: Mol. Microbiol. doi: 10.1046/j.1365-2958.2003.03772.x – volume: 97 start-page: 13324 year: 2000 ident: 2023012511540714100_B18 article-title: Molecular signals required for type III secretion and translocation of the Xanthomonas campestris AvrBs2 protein to pepper plants publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.230450797 – volume: 33 start-page: W72 year: 2005 ident: 2023012511540714100_B3 article-title: SCRATCH: a protein structure and structural feature prediction server publication-title: Nuceic Acids Res. doi: 10.1093/nar/gki396 – volume: 11 start-page: S47 issue: Suppl. 1 year: 2010 ident: 2023012511540714100_B32 article-title: Computational prediction of type III secreted proteins from gram-negative bacteria publication-title: BMC bioinformatics doi: 10.1186/1471-2105-11-S1-S47 – volume: 4 start-page: e4920 year: 2009 ident: 2023012511540714100_B28 article-title: Computational identification of protein methylation sites through bi-profile Bayes feature extraction publication-title: PLoS ONE doi: 10.1371/journal.pone.0004920 – volume-title: Learning with Kernels. year: 2002 ident: 2023012511540714100_B26 – volume: 4 start-page: e5917 year: 2009 ident: 2023012511540714100_B15 article-title: Prediction of type III secretion signals in genomes of gram-negative bacteria publication-title: PLoS ONE doi: 10.1371/journal.pone.0005917 – volume: 292 start-page: 159 year: 2002 ident: 2023012511540714100_B7 article-title: Type IV secretion systems in pathogenic bacteria publication-title: Int. J. Med. Microbiol. doi: 10.1078/1438-4221-00199 – volume: 46 start-page: 769 year: 2002 ident: 2023012511540714100_B23 article-title: Molecular and functional analysis of the type III secretion signal of the Salmonella enterica InvJ protein publication-title: Mol. Microbiol. doi: 10.1046/j.1365-2958.2002.03196.x – volume: 186 start-page: 543 year: 2004 ident: 2023012511540714100_B27 article-title: Pseudomonas syringae type III secretion system targeting signals and novel effectors studied with a Cya translocation reporter publication-title: J. Bacteriol. doi: 10.1128/JB.186.2.543-555.2004 – volume: 14 start-page: 1188 year: 2004 ident: 2023012511540714100_B4 article-title: WebLogo: a sequence logo generator publication-title: Genome Res. doi: 10.1101/gr.849004 – volume: 5 start-page: e1000376 year: 2009 ident: 2023012511540714100_B1 article-title: Sequence-based prediction of type III secreted proteins publication-title: PLoS pathogens doi: 10.1371/journal.ppat.1000376 – volume: 43 start-page: 51 year: 2002 ident: 2023012511540714100_B14 article-title: Molecular characterization of type III secretion signals via analysis of synthetic N-terminal amino acid sequences publication-title: Mol. Microbiol. doi: 10.1046/j.1365-2958.2002.02738.x – volume: 103 start-page: 14941 year: 2006 ident: 2023012511540714100_B30 article-title: An extensive repertoire of type III secretion effectors in Escherichia coli O157 and the role of lambdoid phages in their dissemination publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.0604891103 – volume: 68 start-page: 692 year: 2004 ident: 2023012511540714100_B9 article-title: Type V protein secretion pathway: the autotransporter story publication-title: Mol. Biol. Rev. doi: 10.1128/MMBR.68.4.692-744.2004 – volume: 58 start-page: 267 year: 2005 ident: 2023012511540714100_B20 article-title: A genome-wide screen identifies a Bordetella type III secretion effector and candidate effectors in other species publication-title: Mol. Microbiol. doi: 10.1111/j.1365-2958.2005.04823.x – volume: 39 start-page: 520 year: 2001 ident: 2023012511540714100_B13 article-title: Yersinia YopE is targeted for type III secretion by N-terminal, not mRNA, signals publication-title: Mol. Microbiol. doi: 10.1046/j.1365-2958.2001.02271.x – volume: 14 start-page: 755 year: 1998 ident: 2023012511540714100_B5 article-title: Profile hidden Markov models publication-title: Bioinformatics doi: 10.1093/bioinformatics/14.9.755 – volume: 23 start-page: 251 year: 2010 ident: 2023012511540714100_B19 article-title: Genome-wide identification of a large repertoire of Ralstonia solanacearum type III effector proteins by a new functional screen publication-title: Mol. Plant Microbe Interact. doi: 10.1094/MPMI-23-3-0251 – volume: 11 start-page: 3 year: 2008 ident: 2023012511540714100_B2 article-title: Type VI secretion: a beginner's guide publication-title: Curr. Opin. Microbiol. doi: 10.1016/j.mib.2008.01.006 – volume: 62 start-page: 379 year: 1998 ident: 2023012511540714100_B10 article-title: Type III protein secretion systems in bacterial pathogens of animals and plants publication-title: Mol. Biol. Rev. doi: 10.1128/MMBR.62.2.379-433.1998
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Snippet	Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important... Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in...
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SubjectTerms	Algorithms Amino Acid Sequence Amino acids Bacteria Bacterial Proteins - chemistry Bacterial Proteins - classification Bacterial Secretion Systems Bayes Theorem Bioinformatics Biological and medical sciences Composition effects Computational Biology - methods Effectors Fundamental and applied biological sciences. Psychology General aspects Genome, Bacterial Mathematical models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Statistical Proteins Ralstonia solanacearum Ralstonia solanacearum - chemistry Secretions Sequence Analysis, Protein - methods Software Support vector machines
Title	High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles
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