169 related articles for article (PubMed ID: 27713481)
1. Computational prediction shines light on type III secretion origins.
Goldberg T; Rost B; Bromberg Y
Sci Rep; 2016 Oct; 6():34516. PubMed ID: 27713481
[TBL] [Abstract][Full Text] [Related]
2. Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes.
Hobbs CK; Porter VL; Stow ML; Siame BA; Tsang HH; Leung KY
BMC Genomics; 2016 Dec; 17(1):1048. PubMed ID: 27993130
[TBL] [Abstract][Full Text] [Related]
3. A global survey of bacterial type III secretion systems and their effectors.
Hu Y; Huang H; Cheng X; Shu X; White AP; Stavrinides J; Köster W; Zhu G; Zhao Z; Wang Y
Environ Microbiol; 2017 Oct; 19(10):3879-3895. PubMed ID: 28401683
[TBL] [Abstract][Full Text] [Related]
4. Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems.
Zalguizuri A; Caetano-Anollés G; Lepek VC
Brief Bioinform; 2019 Jul; 20(4):1395-1402. PubMed ID: 29394318
[TBL] [Abstract][Full Text] [Related]
5. Identification and characterization of putative Aeromonas spp. T3SS effectors.
Rangel LT; Marden J; Colston S; Setubal JC; Graf J; Gogarten JP
PLoS One; 2019; 14(6):e0214035. PubMed ID: 31163020
[TBL] [Abstract][Full Text] [Related]
6. Sequence-based prediction of type III secreted proteins.
Arnold R; Brandmaier S; Kleine F; Tischler P; Heinz E; Behrens S; Niinikoski A; Mewes HW; Horn M; Rattei T
PLoS Pathog; 2009 Apr; 5(4):e1000376. PubMed ID: 19390696
[TBL] [Abstract][Full Text] [Related]
7. Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.
An Y; Wang J; Li C; Leier A; Marquez-Lago T; Wilksch J; Zhang Y; Webb GI; Song J; Lithgow T
Brief Bioinform; 2018 Jan; 19(1):148-161. PubMed ID: 27777222
[TBL] [Abstract][Full Text] [Related]
8. Computational prediction of type III and IV secreted effectors in gram-negative bacteria.
McDermott JE; Corrigan A; Peterson E; Oehmen C; Niemann G; Cambronne ED; Sharp D; Adkins JN; Samudrala R; Heffron F
Infect Immun; 2011 Jan; 79(1):23-32. PubMed ID: 20974833
[TBL] [Abstract][Full Text] [Related]
9. Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework.
Dhroso A; Eidson S; Korkin D
Sci Rep; 2018 Nov; 8(1):17209. PubMed ID: 30464223
[TBL] [Abstract][Full Text] [Related]
10. Prediction of type III secretion signals in genomes of gram-negative bacteria.
Löwer M; Schneider G
PLoS One; 2009 Jun; 4(6):e5917. PubMed ID: 19526054
[TBL] [Abstract][Full Text] [Related]
11. A Method for Characterizing the Type III Secretion System's Contribution to Pathogenesis: Homologous Recombination to Generate Yersinia pestis Type III Secretion System Mutants.
Osei-Owusu P; Nilles ML; Bradley DS; Alvine TD
Methods Mol Biol; 2017; 1531():155-164. PubMed ID: 27837489
[TBL] [Abstract][Full Text] [Related]
12. The Bordetella Secreted Regulator BspR Is Translocated into the Nucleus of Host Cells via Its N-Terminal Moiety: Evaluation of Bacterial Effector Translocation by the Escherichia coli Type III Secretion System.
Abe A; Nishimura R; Tanaka N; Kurushima J; Kuwae A
PLoS One; 2015; 10(8):e0135140. PubMed ID: 26247360
[TBL] [Abstract][Full Text] [Related]
13. A new feature selection method for computational prediction of type III secreted effectors.
Yang Y; Qi S
Int J Data Min Bioinform; 2014; 10(4):440-54. PubMed ID: 25946888
[TBL] [Abstract][Full Text] [Related]
14. Analysis of Type III Secretion System Secreted Proteins.
Condry DL; Nilles ML
Methods Mol Biol; 2017; 1531():93-99. PubMed ID: 27837484
[TBL] [Abstract][Full Text] [Related]
15. A genome-wide screen identifies a Bordetella type III secretion effector and candidate effectors in other species.
Panina EM; Mattoo S; Griffith N; Kozak NA; Yuk MH; Miller JF
Mol Microbiol; 2005 Oct; 58(1):267-79. PubMed ID: 16164564
[TBL] [Abstract][Full Text] [Related]
16. EffectiveDB--updates and novel features for a better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems.
Eichinger V; Nussbaumer T; Platzer A; Jehl MA; Arnold R; Rattei T
Nucleic Acids Res; 2016 Jan; 44(D1):D669-74. PubMed ID: 26590402
[TBL] [Abstract][Full Text] [Related]
17. Type VI Secretion Effectors: Methodologies and Biology.
Lien YW; Lai EM
Front Cell Infect Microbiol; 2017; 7():254. PubMed ID: 28664151
[TBL] [Abstract][Full Text] [Related]
18. HrcU and HrpP are pathogenicity factors in the fire blight pathogen Erwinia amylovora required for the type III secretion of DspA/E.
McNally RR; Zeng Q; Sundin GW
BMC Microbiol; 2016 May; 16():88. PubMed ID: 27206522
[TBL] [Abstract][Full Text] [Related]
19. Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.
Dong X; Zhang YJ; Zhang Z
PLoS One; 2013; 8(2):e56632. PubMed ID: 23437191
[TBL] [Abstract][Full Text] [Related]
20. Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector-Host Protein-Protein Interactions.
Meyer M; Ryck J; Goormachtig S; Van Damme P
Int J Mol Sci; 2020 Sep; 21(18):. PubMed ID: 32961832
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
