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Transmembrane beta-barrels (TMBBs) are a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. It is estimated that 2-3% of a bacterial genome encodes TMBBs, yet less than 40 non-redundant structures have been solved. It would be highly advantageous to have methods to rapidly identify TMBBs from increasingly available genomic databases. A prediction algorithm proposed by Wimley in 2002 was based on the physicochemical properties of TMBBs of known structure. This method used relative amino acid abundances to predict the position of beta-strands and beta-hairpins, which are the major structural subunits of TMBBs, and a mathematical simplification of the topology prediction data called a beta-barrel score. To test the accuracy of this algorithm we scored proteins from a non-redundant database of protein sequences from the Protein Data Bank (NRPDB). The results revealed that the algorithm's ability to discriminate true TMBBs from other proteins, while strong, could be significantly improved. First, we updated the relative amino acid abundances to include the latest structural information. Second, we altered the beta-strand prediction method to account for the fact that certain amino acids have a higher propensity to situate near the lipid/water interface than in the hydrophobic core of the bilayer. Third, we adjusted the calculation of the beta-barrel score to address the lowered beta-hairpin density of larger TMBBs such as BtuB. We reanalyzed the NRPDB and the modifications resulted in a 5-fold decrease in the number of false positives, many of which are either non-bacterial proteins or from Gram-positive organisms. We will use this method to analyze the available genomes of Gram-negative bacteria and the results, along with the signal peptide predictions of SignalP (Bendtsen, et al. 2004) will be deposited into a publicly available database.

The transmembrane β-barrel (TMBB) is one of the two major structural motifs found in membrane-spanning proteins. TMBBs are found exclusively in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. Because TMBBs perform a vast array of functions (e.g. signal transduction and cellular adhesion) and are surface-exposed, they present an exploitable vulnerability in drug-resistant pathogenic Gram-negative bacteria. Technical deficiencies have impeded progress in the structural study of TMBBs, which is unfortunate given the magnitude of their biological significance. Although structural proteomics projects are currently underway to help solve the structures of unknown membrane proteins, the computational prediction of TMBBs has improved rapidly to compensate for the limits of empirical methods. To this end, our lab has developed one of the most accurate TMBB prediction programs available, which was used to build a database of TMBB predictions called TMBB-DB. This is a comprehensive database featuring TMBB prediction data from the proteomes of over 500 species of bacteria with nearly 1.9 million total sequences. Combining the TMBB prediction data with signal peptide prediction data generated using SignalP we predicted that more than 3% of the sequences encoded TMBBs, which on average is more than double the number of known or predicted TMBBs already annotated in the proteomes. Users will have access to an in-depth analysis of each sequence and be allowed to analyze sequences not included in the database using the same prediction tools. This database can be useful in directing the efforts of structural proteomics projects, antimicrobial drug therapy design, or vaccine development.

Transmembrane β-barrels (TMBB) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from iron acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. Membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches due to a variety of technical limitations. However, in silico prediction methods have been considered for handling the task of identifying the enigmatic sequences which fold into TMBBs. A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The algorithm's prediction efficiency was tested using a non-redundant set of sequences from proteins of known structure. The algorithm was based on the work of Wimley (2002), but was greatly improved because of its disappointingly high false-positive prediction rate and thusly renamed the Freeman-Wimley algorithm. The improved prediction algorithm developed in this study was shown to be more accurate than previously published prediction methods. Its accuracy is 99% when using the most efficient prediction criteria, i.e. the threshold where the most known TMBBs are correctly predicted and the most non-TMBBs are correctly excluded. The Freeman-Wimley algorithm was used to make predictions in 611 bacterial chromosomes, where an average of 3% of the genes in a given genome encoded TMBBs.