Analyzing patterns between regular secondary structures using short structural building blocks defined by a hidden Markov model

Abstract.

Hidden Markov models were used to identify recurrent short 3D structural building blocks (SSBBs) describing protein backbones. Polypeptide chains were broken down into successive short segments defined by their inter-alpha-carbon distances. Fitting the model to a database of nonredundant proteins identified 12 distinct SSBBs and described the preferred pathways by which SSBBs were assembled to form the 3D structure of the proteins. Protein backbones were labelled in terms of these SSBBs. The observed SSBB preferences for fragments located between regular secondary structures suggested that they depended more on the following regular structure than on the preceding one. Extraction of repeated series of SSBBs between regular secondary structures showed some structural specificity within different connection types. These results confirm that SSBBs can be used as building blocks for analyzing protein structures, and can yield new information on the structures of the coils flanking secondary structures.