A Highly Accurate Statistical Approach for the Prediction of Transmembrane β-Barrels

Abstract

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.