General features of transmembrane beta barrels from a large database.

Abstract

Large datasets for particular classes of proteins can contribute a birds-eye view that is more accurate than that gathered from studying particular exemplars. We used co-evolution data in the form of predicted contact maps to create a large, high-quality database of transmembrane β-barrels (TMBB). We classify sequences for our database using IsItABarrel, an approach combining topology prediction and rule-based TMBB classification. By applying simple feature detection on generated contact maps, our method achieves 96% balanced accuracy when discriminating this structural fold from other protein classes. Our database contains almost two million bacterial TMBB proteins--significantly larger and more accurate than previous datasets. We assessed the TMBB-content of 2,959 proteomes of bacterial organisms across several taxonomic categories and find tremendous variance among TMBB-containing organisms with some having as many as 300 TMBBs (6.79% of proteome) and others as few as 10 (0.27% of proteome). The distribution of the lengths of the TMBBs is suggestive of previously hypothesized duplication events. In addition, we find that the C-terminal β-signal varies among different classes of bacteria and type of protein though it is most commonly GHyGHyGφ+φ. The database is available through our IsItABarrel webapp and we anticipate that its quality and size will serve as a useful resource where high quality TMBB sequence data is required.