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Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation.

E-komon T, Burchmore R, Herzyk P, Davies R - BMC Bioinformatics (2012)

Bottom Line: The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction.The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Sir Graeme Davies Building, Glasgow G12 8QQ, UK.

ABSTRACT

Background: Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.

Results: In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes.

Conclusions: The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria.

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Comparison of OMPs predicted in the present study to those in previously published research. Diagram comparing the numbers of OMPs predicted in the present study with those predicted by Al-hasani et al. [51] and reported by Hatfaludi et al. [56]. Indicated are the numbers of proteins predicted/reported by one, two or all three studies. The total number of proteins predicted/reported by each of the three studies is shown in parentheses.
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Figure 8: Comparison of OMPs predicted in the present study to those in previously published research. Diagram comparing the numbers of OMPs predicted in the present study with those predicted by Al-hasani et al. [51] and reported by Hatfaludi et al. [56]. Indicated are the numbers of proteins predicted/reported by one, two or all three studies. The total number of proteins predicted/reported by each of the three studies is shown in parentheses.

Mentions: Eighty-four of the 98 putative OMPs predicted from the avian strain genome in the present study were also identified in the previous study by Al-hasani et al. [51] (Figure 8). These authors predicted 129 putative OMPs and secreted proteins from the avian P. multocida genome using only three predictors (PA, PSORTb and LIPOP). The additional 14 proteins that we identified included seven proteins predicted by transmembrane β-barrel protein predictors (a pilus assembly protein RcpC, a sialidase NanB, Mce/PqiB, YccT, PM0519, PM1515, PM1772), three proteins predicted by lipoprotein predictors (PM1002, PM1798, PM1939), three proteins predicted by subcellular localization predictors (PM0015, PM0234, a RlpA-like protein PM1926), and one protein (PM1323) predicted by all these predictor groups. In contrast to the present study, Al-hasani et al. [51] did not apply consensus prediction to filter their predicted results and were interested in identifying both OMPs and secreted proteins. Consequently, there was disagreement in the localization of 19 proteins between the three predictors (particularly between PA and PSORTb) and these proteins could not be concluded to be OMPs with certainty. Forty-five proteins predicted by Al-hasani et al. [51] were not confidently predicted in the present study (Figure 8). Of these, 18 were not predicted and 27 were filtered out by consensus prediction or manual confirmation. Clearly, the use of a large number of predictors, together with consensus prediction, allowed us to identify a larger number of outer membrane-associated proteins with a greater degree of confidence.


Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation.

E-komon T, Burchmore R, Herzyk P, Davies R - BMC Bioinformatics (2012)

Comparison of OMPs predicted in the present study to those in previously published research. Diagram comparing the numbers of OMPs predicted in the present study with those predicted by Al-hasani et al. [51] and reported by Hatfaludi et al. [56]. Indicated are the numbers of proteins predicted/reported by one, two or all three studies. The total number of proteins predicted/reported by each of the three studies is shown in parentheses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3403877&req=5

Figure 8: Comparison of OMPs predicted in the present study to those in previously published research. Diagram comparing the numbers of OMPs predicted in the present study with those predicted by Al-hasani et al. [51] and reported by Hatfaludi et al. [56]. Indicated are the numbers of proteins predicted/reported by one, two or all three studies. The total number of proteins predicted/reported by each of the three studies is shown in parentheses.
Mentions: Eighty-four of the 98 putative OMPs predicted from the avian strain genome in the present study were also identified in the previous study by Al-hasani et al. [51] (Figure 8). These authors predicted 129 putative OMPs and secreted proteins from the avian P. multocida genome using only three predictors (PA, PSORTb and LIPOP). The additional 14 proteins that we identified included seven proteins predicted by transmembrane β-barrel protein predictors (a pilus assembly protein RcpC, a sialidase NanB, Mce/PqiB, YccT, PM0519, PM1515, PM1772), three proteins predicted by lipoprotein predictors (PM1002, PM1798, PM1939), three proteins predicted by subcellular localization predictors (PM0015, PM0234, a RlpA-like protein PM1926), and one protein (PM1323) predicted by all these predictor groups. In contrast to the present study, Al-hasani et al. [51] did not apply consensus prediction to filter their predicted results and were interested in identifying both OMPs and secreted proteins. Consequently, there was disagreement in the localization of 19 proteins between the three predictors (particularly between PA and PSORTb) and these proteins could not be concluded to be OMPs with certainty. Forty-five proteins predicted by Al-hasani et al. [51] were not confidently predicted in the present study (Figure 8). Of these, 18 were not predicted and 27 were filtered out by consensus prediction or manual confirmation. Clearly, the use of a large number of predictors, together with consensus prediction, allowed us to identify a larger number of outer membrane-associated proteins with a greater degree of confidence.

Bottom Line: The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction.The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Sir Graeme Davies Building, Glasgow G12 8QQ, UK.

ABSTRACT

Background: Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.

Results: In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes.

Conclusions: The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria.

Show MeSH
Related in: MedlinePlus