title: Automatic classification and analysis of alpha alpha-turn motifs in proteins.
abstract: An automatic procedure for the classification of short protein fragments, representing turn motifs between two consecutive secondary structures, is presented. This procedure has two steps. Fragments of given length are first grouped on the basis of their backbone dihedral angle values, and then clustered as a function of the root-mean-square deviation of their superimposed backbone atoms. The classification procedure identifies 63 families of turn motifs with at least five members, in a dataset of 141 proteins. A detailed analysis is presented of the ten identified alpha alpha-turn families, of which four correspond to novel motifs. The sequence and structure features that characterize these families are described. It is found that some features are conserved within the fragments belonging to the same family, but their environment in the parent protein varies considerably. N-capping interactions and helix stop signals are encountered in a number of families, where they seem to stabilize the motif conformation. In two families, one with three residues in the loop, and one with four, an appreciable fraction of the members displays both types of characteristic helix end interactions in the same motif. Interestingly, contrary to most other alpha alpha-turns, the relative frequency of these two motifs is much higher than that of short protein segments with the same loop conformation. Furthermore, the family with three residues in the loop includes the helix-turn-helix motif known to bind DNA. It seems to be the only one among the ten identified families that can be related to biological function.
authors.affiliation: Unité de Conformation des Macromolécules Biologiques Université Libre de Bruxelles, Belgium.
authors.forename: R T
authors.initials: RT
authors.lastname: Wintjens
authors.forename: M J
authors.initials: MJ
authors.lastname: Rooman
authors.forename: S J
authors.initials: SJ
authors.lastname: Wodak
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D017124:Conserved Sequence; D016208:Databases, Factual; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins