General Features of Transmembrane Beta Barrels from a Large Database
Daniel MontezanoiD, Rebecca Bernstein, Matthew M. CopelandiD, Joanna S.G. SluskyiD
Abstract
Large datasets contribute new insights to subjects formerly investigated by exemplars. We used co-evolution data to create a large, high-quality database of transmembrane {beta}-barrels (TMBB). By applying simple feature detection on generated evolutionary contact maps, our method (IsItABarrel) achieves 95.88% balanced accuracy when discriminating among protein classes. Moreover, comparison with IsItABarrel revealed a high rate of false positives in previous TMBB algorithms. In addition to being more accurate than previous datasets, our database (available online) contains 1,894,206 bacterial TMBB proteins from 20 evolutionary classes, respectively 17 and 2.2 times larger than the previous sets TMBB-DB and OMPdb. We anticipate that due to its quality and size the database will serve as a useful resource where high quality TMBB sequence data is required. We found that TMBBs can be divided into 10 types, three of which have not been previously reported. We find tremendous variance in proteome percentage among TMBB-containing organisms with some using 6.79% of their proteome for TMBBs and others using as little as 0.27% of their proteome. The distribution of the lengths of the TMBBs is suggestive of previously hypothesized duplication events. In addition, we find that the C-terminal {beta}-signal varies among different classes of bacteria though it is most commonly HyGHyGY+F. However, this {beta}-signal is only characteristic of prototypical TMBBs. The nine non-prototypical barrel types have other C-terminal motifs and it remains to be determined if these alternative motifs facilitate TMBB insertion or perform any other signaling function. Significance StatementOuter membrane proteins (OMPs) control all interactions between Gram negative bacteria and their environments including uptake and efflux of antibiotics. We created an algorithm that identifies bacterial OMPs from sequence. The quality of our algorithm allows us to identify most OMPs ([~]1.8 million) from prokaryotic genomes including >240,000 unrelated to previously structurally-resolved OMPs. We identify ten types of OMPs in our database. The largest types signal sequence--used for targeting the membrane-insertion machinery--varies by phylogenetic class. All other types of OMPs do not have a related signal sequence, raising new questions of how these proteins fold. Our web-accessible database will allow for further exploration of the varieties of outer membrane proteins to uncover new targets for controlling antibiotic resistance.
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