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Prediction of type III secretion signals in genomes of gram-negative bacteria.

Löwer M, Schneider G - PLoS ONE (2009)

Bottom Line: Common sequence features were most pronounced in the first 30 amino acids of the effector sequences.Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%).We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes.

View Article: PubMed Central - PubMed

Affiliation: Johann Wolfgang Goethe-University, Chair for Chem- and Bioinformatics, Frankfurt, Germany.

ABSTRACT

Background: Pathogenic bacteria infecting both animals as well as plants use various mechanisms to transport virulence factors across their cell membranes and channel these proteins into the infected host cell. The type III secretion system represents such a mechanism. Proteins transported via this pathway ("effector proteins") have to be distinguished from all other proteins that are not exported from the bacterial cell. Although a special targeting signal at the N-terminal end of effector proteins has been proposed in literature its exact characteristics remain unknown.

Methodology/principal findings: In this study, we demonstrate that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques. Known protein effectors were compiled from the literature and sequence databases, and served as training data for artificial neural networks and support vector machine classifiers. Common sequence features were most pronounced in the first 30 amino acids of the effector sequences. Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%).

Conclusions/significance: We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes. Our study demonstrates that the analyzed signal features are common across a wide range of species, and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. The prediction software is publicly accessible from our web server (www.modlab.org).

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The bacterial type III secretion system (T3SS) forms a translocator complex spanning the bacterial and the host cell membranes for protein translocation.(a) Schematic T3SS structure together with a flagella apparatus (shaded in light grey). The nine components being conserved among T3SS are named in Yersinia nomenclature. In flagella apparati, proteins of the axial structure are exported via a T3SS, e.g. flagellins. Note that T3SS injection needle and translocator complex are not present in flagella (adapted from Sheng et al. [5] and Pallen and Matzke [4]). (b) Comparison of the features of classic signal peptides (top) [12] and the proposed features of T3SS signals (bottom). Both kinds of signals are located at the N-terminal end of a protein.
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pone-0005917-g001: The bacterial type III secretion system (T3SS) forms a translocator complex spanning the bacterial and the host cell membranes for protein translocation.(a) Schematic T3SS structure together with a flagella apparatus (shaded in light grey). The nine components being conserved among T3SS are named in Yersinia nomenclature. In flagella apparati, proteins of the axial structure are exported via a T3SS, e.g. flagellins. Note that T3SS injection needle and translocator complex are not present in flagella (adapted from Sheng et al. [5] and Pallen and Matzke [4]). (b) Comparison of the features of classic signal peptides (top) [12] and the proposed features of T3SS signals (bottom). Both kinds of signals are located at the N-terminal end of a protein.

Mentions: There are six known types of secretion systems in Gram-negative bacteria [1]. Among these, several prediction systems are available for the sec pathway that can be used to recognize N-terminal secretion signals (signal peptides) [2]. Predicting proteins that are secreted via other pathways has recently become a major goal of bioinformatics research [3]. The multi sub-unit type III secretion systems (T3SS) contribute to flagellar biosynthesis [4] and interaction with eukaryotic cells (Figure 1a) [5] and are therefore often involved in pathogenicity of the corresponding bacterial species, e.g. Yersinia pestis, Salmonella enterica, and Escherichia coli [6], [7].


Prediction of type III secretion signals in genomes of gram-negative bacteria.

Löwer M, Schneider G - PLoS ONE (2009)

The bacterial type III secretion system (T3SS) forms a translocator complex spanning the bacterial and the host cell membranes for protein translocation.(a) Schematic T3SS structure together with a flagella apparatus (shaded in light grey). The nine components being conserved among T3SS are named in Yersinia nomenclature. In flagella apparati, proteins of the axial structure are exported via a T3SS, e.g. flagellins. Note that T3SS injection needle and translocator complex are not present in flagella (adapted from Sheng et al. [5] and Pallen and Matzke [4]). (b) Comparison of the features of classic signal peptides (top) [12] and the proposed features of T3SS signals (bottom). Both kinds of signals are located at the N-terminal end of a protein.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2690842&req=5

pone-0005917-g001: The bacterial type III secretion system (T3SS) forms a translocator complex spanning the bacterial and the host cell membranes for protein translocation.(a) Schematic T3SS structure together with a flagella apparatus (shaded in light grey). The nine components being conserved among T3SS are named in Yersinia nomenclature. In flagella apparati, proteins of the axial structure are exported via a T3SS, e.g. flagellins. Note that T3SS injection needle and translocator complex are not present in flagella (adapted from Sheng et al. [5] and Pallen and Matzke [4]). (b) Comparison of the features of classic signal peptides (top) [12] and the proposed features of T3SS signals (bottom). Both kinds of signals are located at the N-terminal end of a protein.
Mentions: There are six known types of secretion systems in Gram-negative bacteria [1]. Among these, several prediction systems are available for the sec pathway that can be used to recognize N-terminal secretion signals (signal peptides) [2]. Predicting proteins that are secreted via other pathways has recently become a major goal of bioinformatics research [3]. The multi sub-unit type III secretion systems (T3SS) contribute to flagellar biosynthesis [4] and interaction with eukaryotic cells (Figure 1a) [5] and are therefore often involved in pathogenicity of the corresponding bacterial species, e.g. Yersinia pestis, Salmonella enterica, and Escherichia coli [6], [7].

Bottom Line: Common sequence features were most pronounced in the first 30 amino acids of the effector sequences.Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%).We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes.

View Article: PubMed Central - PubMed

Affiliation: Johann Wolfgang Goethe-University, Chair for Chem- and Bioinformatics, Frankfurt, Germany.

ABSTRACT

Background: Pathogenic bacteria infecting both animals as well as plants use various mechanisms to transport virulence factors across their cell membranes and channel these proteins into the infected host cell. The type III secretion system represents such a mechanism. Proteins transported via this pathway ("effector proteins") have to be distinguished from all other proteins that are not exported from the bacterial cell. Although a special targeting signal at the N-terminal end of effector proteins has been proposed in literature its exact characteristics remain unknown.

Methodology/principal findings: In this study, we demonstrate that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques. Known protein effectors were compiled from the literature and sequence databases, and served as training data for artificial neural networks and support vector machine classifiers. Common sequence features were most pronounced in the first 30 amino acids of the effector sequences. Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%).

Conclusions/significance: We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes. Our study demonstrates that the analyzed signal features are common across a wide range of species, and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. The prediction software is publicly accessible from our web server (www.modlab.org).

Show MeSH
Related in: MedlinePlus