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...

Full description

Saved in:

Bibliographic Details
Published in:	Bioinformatics Vol. 27; no. 6; pp. 777 - 784
Main Authors:	Wang, Yejun, Zhang, Qing, Sun, Ming-an, Guo, Dianjing
Format:	Journal Article
Language:	English
Published:	Oxford Oxford University Press 15.03.2011
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

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	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. 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. 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. 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.
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
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23955842$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/21233168$$D View this record in MEDLINE/PubMed
BookMark	eNqFkk9v1DAQxSNURP_ARwD5gugl1I5jxxEnVEG7UiUucI4m4zEYJXGwvYeV-PB42W0RHOjFY2t-b56seefVyRIWqqqXgr8VvJdXow9-cSHOkD2mqzFH3ogn1ZmQuqtbI8TJw53L0-o8pe-cc8WVfladNqKRUmhzVv289V-_1YC4jYA7tkayHrMPCwuOjYCZooeJ5d1KbLPZsEQYKZNl5BxhDjEVKpV3Uawh-b20Tiuhdx4ZzH4JDNBbhmG-7xeX4PxE6Xn11MGU6MWxXlRfPn74fH1b33262Vy_v6tRCZ1r03EgpEa1pnedxVYpg0I6khb70VkBjTbd2DhqFTTEyTQt9CCsaY21pORF9eYwtxj_2FLKw-wT0jTBQmGbBqN7wzUX4nFS6U53vG0KeflfUuhOSFPOtqCvjuh2nMkOa_QzxN1wv4UCvD4CkBAmF2FBn_5wsi8__u2pDhzGkFIk94AIPuxTMfydiuGQiqJ7948OfYb9KnIEPz2i_gUK5Mc4
CitedBy_id	crossref_primary_10_1016_S2095_3119_19_62883_2 crossref_primary_10_1038_srep34516 crossref_primary_10_1016_j_ab_2024_115648 crossref_primary_10_3390_ijms150610410 crossref_primary_10_3390_microbiolres15040145 crossref_primary_10_1038_s41598_018_33874_1 crossref_primary_10_1155_2012_696190 crossref_primary_10_1007_s42360_020_00290_3 crossref_primary_10_1016_j_compbiomed_2023_106638 crossref_primary_10_1186_1471_2105_13_66 crossref_primary_10_1074_jbc_M114_633677 crossref_primary_10_1093_femsle_fnx276 crossref_primary_10_1128_AEM_00537_16 crossref_primary_10_3389_fpls_2024_1400659 crossref_primary_10_1128_msystems_01004_23 crossref_primary_10_1111_1462_2920_13755 crossref_primary_10_1093_bib_bby009 crossref_primary_10_1105_tpc_112_101329 crossref_primary_10_1186_s12859_022_04604_2 crossref_primary_10_1007_s10096_016_2716_7 crossref_primary_10_7717_peerj_167 crossref_primary_10_3389_fmicb_2016_00387 crossref_primary_10_1371_journal_pone_0035097 crossref_primary_10_1111_mmi_12675 crossref_primary_10_1093_bib_bbab037 crossref_primary_10_3389_fmicb_2021_605782 crossref_primary_10_1128_mSystems_00288_20 crossref_primary_10_3389_fpls_2022_1024405 crossref_primary_10_1016_j_ab_2023_115407 crossref_primary_10_1093_bib_bbx078 crossref_primary_10_1093_bib_bbw100 crossref_primary_10_1155_2021_6690299 crossref_primary_10_2174_0109298673277663240101111507 crossref_primary_10_1002_mbo3_553 crossref_primary_10_1016_j_compbiomed_2019_103374 crossref_primary_10_1016_j_ymeth_2012_06_011 crossref_primary_10_3389_fpls_2016_00126 crossref_primary_10_1093_bib_bbab310 crossref_primary_10_1128_JB_00173_12 crossref_primary_10_1093_bioinformatics_btae378 crossref_primary_10_1007_s40484_019_0184_7 crossref_primary_10_1099_mic_0_001328 crossref_primary_10_1016_j_csbj_2021_03_019 crossref_primary_10_1016_j_ab_2016_06_012 crossref_primary_10_1002_jcb_30642 crossref_primary_10_1371_journal_pone_0059754 crossref_primary_10_1371_journal_pone_0036038 crossref_primary_10_1155_2020_3974598 crossref_primary_10_1155_2014_845479 crossref_primary_10_3390_microorganisms10020260 crossref_primary_10_1586_epr_12_52 crossref_primary_10_1371_journal_pone_0026930 crossref_primary_10_1371_journal_pone_0084439 crossref_primary_10_1016_j_tim_2023_05_011 crossref_primary_10_1093_nargab_lqab086 crossref_primary_10_1042_BJ20150518 crossref_primary_10_1093_nar_gkz924 crossref_primary_10_1186_1471_2105_12_442 crossref_primary_10_1038_srep38741 crossref_primary_10_1371_journal_pone_0058173 crossref_primary_10_1039_C5AN00311C crossref_primary_10_3389_fmicb_2021_813094 crossref_primary_10_1093_bib_bbaa008 crossref_primary_10_1186_s12864_016_3363_1 crossref_primary_10_1038_srep41031 crossref_primary_10_1016_j_compbiolchem_2017_10_004 crossref_primary_10_1186_s12859_018_2321_0 crossref_primary_10_1371_journal_pone_0056632 crossref_primary_10_1016_j_ab_2018_09_007
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
ContentType	Journal Article
Copyright	2015 INIST-CNRS
Copyright_xml	– notice: 2015 INIST-CNRS
DBID	AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7SC 8FD JQ2 L7M L~C L~D 7X8 7QL 7QO 7T7 C1K FR3 P64
DOI	10.1093/bioinformatics/btr021
DatabaseName	CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Environmental Sciences and Pollution Management Engineering Research Database Biotechnology and BioEngineering Abstracts
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional MEDLINE - Academic Biotechnology Research Abstracts Bacteriology Abstracts (Microbiology B) Engineering Research Database Industrial and Applied Microbiology Abstracts (Microbiology A) Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management
DatabaseTitleList	Biotechnology Research Abstracts Computer and Information Systems Abstracts MEDLINE - Academic CrossRef MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	1367-4811 1460-2059
EndPage	784
ExternalDocumentID	21233168 23955842 10_1093_bioinformatics_btr021
Genre	Research Support, Non-U.S. Gov't Journal Article
GroupedDBID	--- -E4 -~X .2P .DC .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN AAYXX ABEJV ABEUO ABIXL ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABZBJ ACGFS ACIWK ACPRK ACUFI ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC AMNDL APIBT APWMN ARIXL ASPBG AVWKF AXUDD AYOIW AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE CITATION COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HW0 HZ~ IOX J21 JXSIZ KAQDR KOP KQ8 KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NU- NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. Q5Y R44 RD5 RNS ROL ROX RPM RUSNO RW1 RXO SV3 TEORI TJP TLC TOX TR2 W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ~91 ~KM .-4 .GJ ABEFU ABGNP ABNGD ABQTQ ACUKT AFFNX AI. AQDSO ATTQO AZFZN ELUNK HVGLF IQODW M49 NTWIH O~Y PB- RIG RNI RZF RZO VH1 ZGI ADRIX AFXEN BCRHZ CGR CUY CVF ECM EIF NPM 482 7SC 8FD ABJNI JQ2 L7M L~C L~D ROZ TN5 WH7 7X8 7QL 7QO 7T7 C1K FR3 P64
ID	FETCH-LOGICAL-c516t-870aece25489f7dc4558c13fe3dc9bfd1a2687b2fe45a2e0e824a9a1d848dde53
ISICitedReferencesCount	91
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000288277300006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1367-4803 1367-4811
IngestDate	Tue Oct 07 09:56:34 EDT 2025 Sun Nov 09 11:29:22 EST 2025 Fri Jul 11 12:37:05 EDT 2025 Wed Feb 19 01:48:35 EST 2025 Wed Apr 02 07:17:09 EDT 2025 Sat Nov 29 05:33:46 EST 2025 Tue Nov 18 22:32:46 EST 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	6
Keywords	Composition Accuracy Position Secretion Aminoacid Prediction Bacteria Profile
Language	English
License	CC BY 4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c516t-870aece25489f7dc4558c13fe3dc9bfd1a2687b2fe45a2e0e824a9a1d848dde53
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
OpenAccessLink	https://academic.oup.com/bioinformatics/article-pdf/27/6/777/48864167/bioinformatics_27_6_777.pdf
PMID	21233168
PQID	1671381674
PQPubID	23500
PageCount	8
ParticipantIDs	proquest_miscellaneous_869806011 proquest_miscellaneous_856767042 proquest_miscellaneous_1671381674 pubmed_primary_21233168 pascalfrancis_primary_23955842 crossref_primary_10_1093_bioinformatics_btr021 crossref_citationtrail_10_1093_bioinformatics_btr021
PublicationCentury	2000
PublicationDate	2011-03-15
PublicationDateYYYYMMDD	2011-03-15
PublicationDate_xml	– month: 03 year: 2011 text: 2011-03-15 day: 15
PublicationDecade	2010
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford – name: England
PublicationTitle	Bioinformatics
PublicationTitleAlternate	Bioinformatics
PublicationYear	2011
Publisher	Oxford University Press
Publisher_xml	– name: Oxford University Press
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
SSID	ssj0005056 ssj0051444
Score	2.3599792
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...
SourceID	proquest pubmed pascalfrancis crossref
SourceType	Aggregation Database Index Database Enrichment Source
StartPage	777
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
URI	https://www.ncbi.nlm.nih.gov/pubmed/21233168 https://www.proquest.com/docview/1671381674 https://www.proquest.com/docview/856767042 https://www.proquest.com/docview/869806011
Volume	27
WOSCitedRecordID	wos000288277300006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 20220930 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLa6ARISQtwpl8pIiJcqLBcndh4RbFCp6kDqoDxFrmOjTiMtvUxD4u_wPzkndpJGU8V44CVqXTtyej77nPhcPkJeRikzCVfai5k2HjNKemmSG1jxIjBCmECXdD6fh3w0EpNJ-rHT-V3lwpyf8aIQFxfp4r-KGtpA2Jg6-w_irm8KDfAZhA5XEDtcryR4jNzwpFKbJTK5L5boianMwqmtzYwB5nj0OhgM-iu0G9HstJEdyL2Dmi1HL0IV0eVhPibGFPXl91kx70s1K5Phqt_7jvl71XIRz-auKut6K6L-izuf_qpPNzUu61PrT5UiLd1ULq6_-OY1EH6_Kc923wGqT6vOeXMKG3k2bdPtsxGWWxe-3du0bWOJD5J0BcLd5mwLBzgQbu-03LG_WKXNLc_cJX1ga2VNWw-MDeulbxOz2xW4R8fZ0clwmI0PJ-NXix8ekpOhE98xteyRayGPU9w8x8eTJpLIx3pE9gvYn8wSKbsHrLLG0uigPY0DO4mWPXRrIVewNI3lVNn90lMaP-M75LZ7a6FvLNruko4u7pEblsf0533yq4U52mCOzg2tMUcRcxQwRyvM0RpztMQchRGXMEdLzFHEHN3CHK0w94CcHB2O337wHK2Hp-IgWYP-9aVWOoR35dTwXLE4FiqIjI5ylU5NHsgwEXwaGs1iGWpfi5DJVAa5YAKUcRw9JPvFvNCPCZV5lIOGymOVJqDxE6Eizqa5FFzE3ASsS1j152bK1bxH6pWzzMZeRFlbJpmVSZe8roctbNGXvw3otSRXjwqjFJ6OhV3yohJlBvs3OuVkoeebVRYkPEDnPYfJ0h19RIxlFX28zc4uSSqwtBJM5ZFFSjMHME6Rnu7JFebwlNxsluszsr9ebvRzcl2dr2erZY_s8YnoleD_AzWB6O8
linkProvider	Oxford University Press
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=High-accuracy+prediction+of+bacterial+type+III+secreted+effectors+based+on+position-specific+amino+acid+composition+profiles&rft.jtitle=Bioinformatics&rft.au=Wang%2C+Yejun&rft.au=Zhang%2C+Qing&rft.au=Sun%2C+Ming-an&rft.au=Guo%2C+Dianjing&rft.date=2011-03-15&rft.issn=1367-4803&rft.eissn=1460-2059&rft.volume=27&rft.issue=6&rft.spage=777&rft.epage=784&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtr021&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon