title: Prediction and classification of alpha-turn types.
abstract: Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns was conducted by V. Pavone et al. [(1996) Biopolymers, Vol. 38, pp. 705-721] from 190 proteins (221 protein chains). They found 356 alpha-turns that were classified into nine different types according to their backbone trajectory features. In view of this new discovery, a sequence-coupled model based on Markov chain theory is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction by resubstitution test and jackknife test imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the role of its interaction with the other part of a protein cannot be completely ignored. The algorithm presented here can also be used to conduct the prediction in which a distinction between alpha-turns and non-alpha-turns is also required.
authors.affiliation: Computer-Aided Drug Discovery, Pharmacia & Upjohn, Kalamazoo, MI 49007-4940, USA.
authors.forename: K C
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001536:Bence Jones Protein / Q000737:chemistry; D006801:Humans; D008390:Markov Chains; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000737:chemistry; D010446:Peptide Fragments / Q000737:chemistry; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D009842:Oligopeptides; D010446:Peptide Fragments; D011506:Proteins; D001536:Bence Jones Protein