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Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.

ABSTRACT: BACKGROUND: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. RESULTS:Using a series of computer-based algorithms, we selected all protein-encoding sequences predicted to be OM-localized and/or to form ?-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. CONCLUSIONS: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.

SUBMITTER: Kenedy MR

PROVIDER: S-EPMC4939628 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.

Kenedy Melisha R MR Scott Edgar J EJ Shrestha Binu B Anand Arvind A Iqbal Henna H Radolf Justin D JD Dyer David W DW Akins Darrin R DR

BMC microbiology 20160711 1

<h4>Background</h4>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.<h4>Results</h4>Using a series of computer-based algorithms, we selected all protein-encoding ...[more]

PMID: 27400788

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Project description:Among bacterial cell envelopes, the Borrelia burgdorferi outer membrane (OM) is structurally unique in that the identities of many protein complexes remain unknown; however, their characterization is the first step toward our understanding of membrane protein interactions and potential functions. Here, we used two-dimensional blue native/SDS-PAGE/mass spectrometric analysis for a global characterization of protein-protein interactions as well as to identify protein complexes in OM vesicles isolated from multiple infectious sensu stricto isolates of B. burgdorferi. Although we uncovered the existence of at least 10 distinct OM complexes harboring several unique subunits, the complexome is dominated by the frequent occurrence of a limited diversity of membrane proteins, most notably P13, outer surface protein (Osp) A, -B, -C, and -D and Lp6.6. The occurrence of these complexes and specificity of subunit interaction were further supported by independent two-dimensional immunoblotting and coimmunoprecipitation assays as well as by mutagenesis studies, where targeted depletion of a subunit member (P66) selectively abolished a specific complex. Although a comparable profile of the OM complexome was detected in two major infectious isolates, such as B31 and 297, certain complexes are likely to occur in an isolate-specific manner. Further assessment of protein complexes in multiple Osp-deficient isolates showed loss of several protein complexes but revealed the existence of additional complex/subunits that are undetectable in wild-type cells. Together, these observations uncovered borrelial antigens involved in membrane protein interactions. The study also suggests that the assembly process of OM complexes is specific and that the core or stabilizing subunits vary between complexes. Further characterization of these protein complexes including elucidation of their biological significance may shed new light on the mechanism of pathogen persistence and the development of preventative measures against the infection.

Project description:BackgroundPrediction 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.ResultsWe 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.ConclusionsThe 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.

Dataset Information

Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.

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Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.

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