Similar to Gram-negative organisms, Borrelia spirochetes are dual-membrane organisms with both an inner and outer membrane. Although the outer membrane contains integral membrane proteins, few of the borrelial outer membrane proteins (OMPs) have been identified and characterized to date. Therefore, we utilized a consensus computational network analysis to identify novel borrelial OMPs.
Using a series of computer-based algorithms, we selected all protein-encoding sequences predicted to be OM-localized and/or to form β-barrels in the borrelial OM. Using this system, we identified 41 potential OMPs from B. burgdorferi and characterized three (BB0838, BB0405, and BB0406) to confirm that our computer-based methodology did, in fact, identify borrelial OMPs. Triton X-114 phase partitioning revealed that BB0838 is found in the detergent phase, which would be expected of a membrane protein. Proteolysis assays indicate that BB0838 is partially sensitive to both proteinase K and trypsin, further indicating that BB0838 is surface-exposed. Consistent with a prior study, we also confirmed that BB0405 is surface-exposed and associates with the borrelial OM. Furthermore, we have shown that BB0406, the product of a co-transcribed downstream gene, also encodes a novel, previously uncharacterized borrelial OMP. Interestingly, while BB0406 has several physicochemical properties consistent with it being an OMP, it was found to be resistant to surface proteolysis. Consistent with BB0405 and BB0406 being OMPs, both were found to be capable of incorporating into liposomes and exhibit pore-forming activity, suggesting that both proteins are porins. Lastly, we expanded our computational analysis to identify OMPs from other borrelial organisms, including both Lyme disease and relapsing fever spirochetes.
Using a consensus computer algorithm, we generated a list of candidate OMPs for both Lyme disease and relapsing fever spirochetes and determined that three of the predicted B. burgdorferi proteins identified were indeed novel borrelial OMPs. The combined studies have identified putative spirochetal OMPs that can now be examined for their roles in virulence, physiology, and disease pathogenesis. Importantly, the studies described in this report provide a framework by which OMPs from any human pathogen with a diderm ultrastructure could be cataloged to identify novel virulence factors and vaccine candidates.

Leptospirosis is a bacterial zoonotic disease caused by an infection with a spirochete belonging to the genus Leptospira. In animals, leptospirosis displays a wide range of pathologies, including fever, abortion, icterus, and uveitis. Conversely, infection in humans is associated with multi-organ injury, resulting in an increased rate of fatalities. Pathogenic leptospires are able to translocate through cell monolayers at a rate significantly greater than that of non-pathogenic leptospires. Thus, vaccine approaches have been focused on targeting bacterial motility, lipopolysaccharides (LPSs), lipoproteins, outer-membrane proteins (OMPs) and other potential virulence factors. Previous studies have indicated that leptospiral proteins elicit long-lasting immunological memory in infected humans. In the study reported here, the efficacy of a synthetic consensus DNA vaccine developed against the Leptospira membrane lipoprotein LipL45 was tested. After in vivo electroporation (EP) mediated intramuscular immunization with a synthetic LipL45 DNA vaccine (pLipL45) immunized mice developed a significant cellular response along with the development of anti-LipL45-specific antibodies. Specifically, the pLipL45 vaccine induced a significant Th1 type immune response, indicated by the higher production of IL-12 and IFN-γ cytokines. The results presented here are the first demonstration that a LipL45 based DNA immunogen has potential as a anti-Leptospira vaccine.

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.

Treponema pallidum reacts poorly with the antibodies present in rabbit and human syphilitic sera, a property attributed to the paucity of proteins in its outer membrane. To better understand the basis for the syphilis spirochete\'s \"stealth pathogenicity,\" we used a dual-label, 3-step amplified assay in which treponemes encapsulated in gel microdroplets were probed with syphilitic sera in parallel with anti-FlaA antibodies. A small (approximately 5 to 10%) but reproducible fraction of intact treponemes bound IgG and/or IgM antibodies. Three lines of evidence supported the notion that the surface antigens were likely β-barrel-forming outer membrane proteins (OMPs): (i) surface labeling with anti-lipoidal (VDRL) antibodies was not observed, (ii) immunoblot analysis confirmed prior results showing that T. pallidum glycolipids are not immunoreactive, and (iii) labeling of intact organisms was not appreciably affected by proteinase K (PK) treatment. With this method, we also demonstrate that TprK (TP0897), an extensively studied candidate OMP, and TP0136, a lipoprotein recently reported to be surface exposed, are both periplasmic. Consistent with the immunolabeling studies, TprK was also found to lack amphiphilicity, a characteristic property of β-barrel-forming proteins. Using a consensus computational framework that combined subcellular localization and β-barrel structural prediction tools, we generated ranked groups of candidate rare OMPs, the predicted T. pallidum outer membrane proteome (OMPeome), which we postulate includes the surface-exposed molecules detected by our enhanced gel microdroplet assay. In addition to underscoring the syphilis spirochete\'s remarkably poor surface antigenicity, our findings help to explain the complex and shifting balance between pathogen and host defenses that characterizes syphilitic infection.

Certain streptococcal M proteins bind collagen via an octapeptide motif that is located in their hypervariable N-terminal region. The interaction with this extracellular matrix protein enhances adhesion to the host tissue and thereby facilitates infection. Moreover, it has the side effect of eliciting collagen autoimmune responses, a phenomenon which is also observed in patients with acute rheumatic fever. Therefore, the octapeptide motif was named peptide associated with rheumatic fever (PARF). Only a comprehensive characterization of the collagen-binding M proteins and their collagen-binding motifs will allow the investigation of their associations with certain streptococcal infections and their sequelae. Therefore, a collection of Streptococcus dysgalactiae equisimilis strains that were isolated from infected humans was examined, in order to identify collagen-binding proteins and motifs. Strains that bound collagen independent of a hyaluronic acid capsule belonged to 7 distinct types of the emm gene, which codes for the M protein (emm types). Only one of these emm types was previously described as collagen-binding. Five possessed a PARF sequence. The other 2 emm types stC2sk.0 and stG2574 had PARF-like motifs that diverged from the previously described consensus sequence AXYLZZLN but were able to induce collagen autoimmunity when injected into mice. The results led to the amended PARF consensus (A/E/T)XYLXXLN. Moreover, they demonstrate a predictive power regarding collagen-binding and elicitation of collagen autoimmunity, indicating that PARF may be one of the markers for strains that cause collagen-dependent acute rheumatic fever.

The ability to search sequence datasets for membrane spanning proteins is an important requirement for genome annotation. However, the development of algorithms to identify novel types of transmembrane beta-barrel (TMB) protein has proven substantially harder than for transmembrane helical proteins, owing to a shorter TM domain in which only alternate residues are hydrophobic. Although recent reports have described important improvements in the development of such algorithms, there is still concern over their ability to confidently screen genomes. Here we describe a new algorithm combining composition and hidden Markov model topology based classifiers (called TMB-Hunt2), which achieves a crossvalidation accuracy of >95%, with 96.7% precision and 94.2% recall. An overview is given of the algorithm design, with a thorough assessment of performance and application to a number of genomes. Of particular note is that TMB/extracellular protein discrimination is significantly more difficult than TMB/cytoplasmic protein discrimination, with the predictor correctly rejecting just 74% of extracellular proteins, in comparison to 98% of cytoplasmic proteins. Focus is given to directions for further improvements in TMB/non-TMB protein discrimination, with a call for the development of standardized tests and assessments of such algorithms. Tools and datasets are made available through a website called TMB-Web (http://www.bioinformatics.leeds.ac.uk/TMB-Web/TMB-Hunt2).

The Campylobacter jejuni pgl locus encodes an N-linked protein glycosylation machinery that can be functionally transferred into Escherichia coli. In this system, we analyzed the elements in the C. jejuni N-glycoprotein AcrA required for accepting an N-glycan. We found that the eukaryotic primary consensus sequence for N-glycosylation is N terminally extended to D/E-Y-N-X-S/T (Y, X not equalP) for recognition by the bacterial oligosaccharyltransferase (OST) PglB. However, not all consensus sequences were N-glycosylated when they were either artificially introduced or when they were present in non-C. jejuni proteins. We were able to produce recombinant glycoproteins with engineered N-glycosylation sites and confirmed the requirement for a negatively charged side chain at position -2 in C. jejuni N-glycoproteins. N-glycosylation of AcrA by the eukaryotic OST in Saccharomyces cerevisiae occurred independent of the acidic residue at the -2 position. Thus, bacterial N-glycosylation site selection is more specific than the eukaryotic equivalent with respect to the polypeptide acceptor sequence.

The Gram-negative pathogen Pseudomonas aeruginosa encodes multiple protein export systems, the substrates of which contain export signals such as N-terminal signal peptides. Here we report the first genome-wide computational and laboratory screen for N-terminal signal peptides in this important opportunistic pathogen. The computational identification of signal peptides was based on a consensus between multiple predictive tools and showed that 38% of the P. aeruginosa PAO1 proteome was predicted to encode exported proteins, most of which utilize cleavable type I signal peptides or uncleavable transmembrane helices. In addition, known and novel lipoproteins (type II), twin arginine transporter (TAT), and prepilin peptidase substrates (type IV) were also identified. A laboratory-based screen using the alkaline phosphatase (PhoA) fusion method was then used to test our predictions. In total, 310 nonredundant PhoA fusions were successfully identified, 296 of which possess a predicted export signal. Analysis of the PhoA fusion proteins lacking an export signal revealed that three proteins have alternate translation start sites that encode signal peptides, two proteins may use an unknown export signal, and the remaining nine proteins are likely cytoplasmic proteins and represent false positives associated with the PhoA screen. Our approach to identify exported proteins illustrates how computational and laboratory-based methods are complementary, where computational analyses provide a large number of accurate predictions while laboratory methods both confirm predictions and reveal unique cases meriting further analysis.

BACKGROUND: Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method.
RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.
CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at http://bioinformatics.biol.uoa.gr/ConBBPRED.

We have determined the transcription start points (tsp) for recently identified Porphyromonas gingivalis W50 genes, kgp, rgpA, rgpB (formerly designated prtK, prtR, and prtRII respectively), fetB and the mcmAB operon. Alignment of the DNA upstream of these tsp and those from the literature has enabled us to identify consensus sequences that may represent a P. gingivalis promoter. There is a potential -10 hexamer sequence, 5\'-TATATT-3\' centred on average at -10/11 nt which is repeated at -19/20 nt and an upstream consensus, 5\'-CAGAT(A/G)-3\' which is centred at -39/40 nt.