Automating the identification and analysis of protein {beta}-barrels

ßBarrels are widespread and well-studied featuresof a great many protein structures. In this paper an unsuper-visedmethod for the detection of P-barrels is developed based ontechniques from graph theory. The hydrogen bonded connectivityof ß-sheets is derived using standard pattern recognitiontechniques and expressed as a graph. Barrels correspond to topologicalrings in these connectivity graphs and can thus be identifiedusing ring perception algorithms. Following from this, the characteristictopological structure of a barrel can be expressed using a novelform of reduced nomenclature that counts sequence separationsbetween successive members of the ring set These techniquesare tested by applying them to the detection of barrels in anon-redundant subset of the Brookhaven database. Results indicatethat topological rings do seem to correspond uniquely to ß-barrelsand that the technique, as implemented, finds the majority ofbarrels present in the dataset.