title: Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation.
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.
authors.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.
authors.forename: Teerasak
authors.initials: T
authors.lastname: E-komon
authors.forename: Richard
authors.initials: R
authors.lastname: Burchmore
authors.forename: Pawel
authors.initials: P
authors.lastname: Herzyk
authors.forename: Robert
authors.initials: R
authors.lastname: Davies
mesh_terms: D000465:Algorithms; D001425:Bacterial Outer Membrane Proteins / Q000032:analysis* / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016979:Pasteurella multocida / Q000737:chemistry* / Q000145:classification / Q000235:genetics / Q000378:metabolism; D017434:Protein Structure, Tertiary; D020543:Proteome / Q000032:analysis*
chemical_list: D001425:Bacterial Outer Membrane Proteins; D020543:Proteome