ß-barrel outer membrane proteins from Gram-negative bacteria are implicated in a broad range of functions crucial for their survival. Such functions include passive nutrient uptake, active transport of large molecules, protein secretion as well as adhesion to host cells, through which bacteria expose their virulence activity. These proteins differ from typical membrane proteins (like the ones found in inner membranes) in that their membrane-spanning regions are formed by amphipathic beta strands instead of alpha helices.
Their biological importance together with the inadequate annotation and classification found in public databases, urges the need for intensive studies and accurate data collection regarding ß-barrel proteins. For constructing OMPdb, multiple freely accessible resources were combined and a detailed literature search was performed. The current database holdings can be seen at the table on the right; the classification of OMPdb's protein entries into families is based mainly on structural and functional criteria. Information included in the database consists of sequence data, as well as annotation for structural characteristics (such as the transmembrane segments), literature references and links to other public databases, features that are unique worldwide.
Along with the database, a collection of profile Hidden Markov Models that were shown to be characteristic for ß-barrel outer membrane proteins was also compiled. This set, when used in combination with our previously developed algorithms (PRED-TMBB, MCMBB and ConBBPRED) will serve as a powerful tool in matters of discrimination and classification of novel ß-barrel proteins and whole-genome analyses.
The web interface of OMPdb offers the user the ability not only to view the available data, but also to submit advanced queries for text search within the database's protein entries or run BLAST searches against the database. The most up-to-date version of the database (as well as all past versions) can be downloaded in various formats (flat text, XML format or raw FASTA sequences).