Abstract
In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.
Original language | English (US) |
---|---|
Pages (from-to) | 1395-1402 |
Number of pages | 8 |
Journal | Briefings in bioinformatics |
Volume | 20 |
Issue number | 4 |
DOIs | |
State | Published - Mar 27 2018 |
Fingerprint
Keywords
- T3SS
- T4SS
- T6SS
- machine learning
- phylogenetic profile
- protein secretion prediction
ASJC Scopus subject areas
- Information Systems
- Molecular Biology
Cite this
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, Andrés; Caetano-Anollés, Gustavo; Lepek, Viviana Claudia.
In: Briefings in bioinformatics, Vol. 20, No. 4, 27.03.2018, p. 1395-1402.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - 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
AU - Zalguizuri, Andrés
AU - Caetano-Anollés, Gustavo
AU - Lepek, Viviana Claudia
PY - 2018/3/27
Y1 - 2018/3/27
N2 - In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.
AB - In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.
KW - T3SS
KW - T4SS
KW - T6SS
KW - machine learning
KW - phylogenetic profile
KW - protein secretion prediction
UR - http://www.scopus.com/inward/record.url?scp=85072984167&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072984167&partnerID=8YFLogxK
U2 - 10.1093/bib/bby009
DO - 10.1093/bib/bby009
M3 - Article
C2 - 29394318
AN - SCOPUS:85072984167
VL - 20
SP - 1395
EP - 1402
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
IS - 4
ER -