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. 2009 Apr;5(4):e1000376.
doi: 10.1371/journal.ppat.1000376. Epub 2009 Apr 24.

Sequence-based prediction of type III secreted proteins

Roland Arnold  1 , Stefan Brandmaier, Frederick Kleine, Patrick Tischler, Eva Heinz, Sebastian Behrens, Antti Niinikoski, Hans-Werner Mewes, Matthias Horn, Thomas Rattei
Affiliations

Sequence-based prediction of type III secreted proteins

Roland Arnold et al. PLoS Pathog. 2009 Apr.

Erratum in

  • PLoS Pathog. 2009 Apr;5(4). doi: 10.1371/annotation/78659a32-7869-4b14-91a6-b301a588d937

Abstract

The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of approximately 71% and selectivity of approximately 85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen-host interactions.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Enrichment of amino acids in effector N-termini.
Amino acids that are significantly enriched or depleted in the first 25 residues of effectors from the animal pathogen effector set and from the plant symbiont effector set (p-Value<0.05 in the one sided Mann-Whitney test in at least one of the sets). Frequencies are given as percentage of amino acids within the 25 first residues. Error bars represent one standard deviation in plus and one standard deviation in minus directions.
Figure 2
Figure 2. Exploration of position and length of the signal.
Exploration of optimal length of the signal (A) and begin position of a 15 amino acid long window (B). The AUC value for each length and begin position is plotted for the animal pathogen set (red) and the plant symbiont set (green).
Figure 3
Figure 3. Taxonomic universality of the signal.
The y-axis denotes the achieved AUC value of EffectiveT3 when trained without the positive and negative samples from the taxonomic group denoted at the bottom of the x-axis and tested against this set. The performance on a randomly chosen set of positives and negatives having the same taxonomic composition is given for comparison.
Figure 4
Figure 4. Overview of EffectiveT3 predictions in complete genomes from Gram-positive bacteria and archaea.
The figure shows the percentage of positive predictions in proteomes from Gram-positive bacteria and archaea, respectively, depending on the G+C content of the genomes. Linear fits are shown by trend lines in the colours of the respective data sets; attached are the coefficients of determination R2 of each fit. The individual results for all proteomes can be found in Table S11.
Figure 5
Figure 5. Overview of EffectiveT3 predictions in complete genomes from Gram-negative bacteria with and without TTSS.
The figure shows the percentage of positive predictions in proteomes from Gram-negative bacteria with and without TTSS, depending on the G+C content of the genomes. The plot has been scaled as Figure 4 to facilitate comparison. Linear fits are shown by trend lines in the colours of the respective data sets; attached are the coefficients of determination R2 of each fit. The individual results for all proteomes can be found in Table S11.
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