A highly accurate statistical approach for the prediction of transmembrane β-barrels

Abstract

Transmembrane beta-barrels (TMBBs) 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 nutrient acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. However, membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches and current prediction methods are still unreliable. A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The Freeman-Wimley prediction algorithm developed in this study has an accuracy of 99% and MCC of 0.748 when using the most efficient prediction criteria, which is better than any previously published algorithm. The MS Windows-compatible application is available for download at http://www.tulane.edu/~biochem/WW/apps.html.