title: Proteomics-based consensus prediction of protein retention in a bacterial membrane.
abstract: The availability of complete bacterial genome sequences allows proteome-wide predictions of exported proteins that are potentially retained in the cytoplasmic membranes of the corresponding organisms. In practice, however, major problems are encountered with the computer-assisted distinction between (Sec-type) signal peptides that direct exported proteins into the growth medium and lipoprotein signal peptides or amino-terminal membrane anchors that cause protein retention in the membrane. In the present studies, which were aimed at improving methods to predict protein retention in the bacterial cytoplasmic membrane, we have compared sets of membrane-attached and extracellular proteins of Bacillus subtilis that were recently identified through proteomics approaches. The results showed that three classes of membrane-attached proteins can be distinguished. Two classes include 43 lipoproteins and 48 proteins with an amino-terminal transmembrane segment, respectively. Remarkably, a third class includes 31 proteins that remain membrane-retained despite the presence of typical Sec-type signal peptides with consensus signal peptidase recognition sites. This unprecedented finding indicates that unknown mechanisms are involved in membrane retention of this class of proteins. A further novelty is a consensus sequence indicative for release of certain lipoproteins from the membrane by proteolytic shaving. Finally, using non-overlapping sets of secreted and membrane-retained proteins, the accuracy of different signal peptide prediction algorithms was assessed. Accuracy for the prediction of protein retention in the membrane was increased to 82% using a majority-vote approach. Our findings provide important leads for future identification of surface proteins from pathogenic bacteria, which are attractive candidate infection markers and potential targets for drugs or vaccines.
authors.affiliation: Department of Clinical Chemistry, Radboud University Nijmegen - Medical Centre, The Netherlands. h.tjalsma@akc.umcn.nl
authors.forename: Harold
authors.initials: H
authors.lastname: Tjalsma
authors.forename: Jan Maarten
authors.initials: JM
authors.lastname: van Dijl
mesh_terms: D000595:Amino Acid Sequence; D001412:Bacillus subtilis / Q000737:chemistry* / Q000502:physiology; D001426:Bacterial Proteins / Q000737:chemistry / Q000302:isolation & purification*; D002462:Cell Membrane / Q000737:chemistry; D016384:Consensus Sequence; D008074:Lipoproteins / Q000737:chemistry / Q000302:isolation & purification; D008565:Membrane Proteins / Q000737:chemistry / Q000302:isolation & purification*; D008954:Models, Biological; D008969:Molecular Sequence Data; D021382:Protein Sorting Signals; D040901:Proteomics / Q000379:methods*; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012697:Serine Endopeptidases
chemical_list: D001426:Bacterial Proteins; D008074:Lipoproteins; D008565:Membrane Proteins; D021382:Protein Sorting Signals; D012697:Serine Endopeptidases; C026515:type I signal peptidase