title: A tetrapeptide-based method for polyproline II-type secondary structure prediction.
abstract: We describe a new method for polyproline II-type (PPII) secondary structure prediction based on tetrapeptide conformation properties using data obtained from all globular proteins in the Protein Data Bank (PDB). This is the first method for PPII prediction with a relatively high level of accuracy (approximately 60%). Our method uses only frequencies of different conformations among oligopeptides without any additional parameters. We also attempted to predict alpha-helices and beta-strands using the same approach. We find that the application of our method reveals interrelation between sequence and structure even for very short oligopeptides (tetrapeptides).
authors.affiliation: Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia. vlasov@imb.ac.ru
authors.forename: Peter K
authors.initials: PK
authors.lastname: Vlasov
authors.forename: Anna V
authors.initials: AV
authors.lastname: Vlasova
authors.forename: Vladimir G
authors.initials: VG
authors.lastname: Tumanyan
authors.forename: Natalia G
authors.initials: NG
authors.lastname: Esipova
mesh_terms: D030562:Databases, Protein; D008958:Models, Molecular; D009842:Oligopeptides / Q000737:chemistry*; D010455:Peptides / Q000737:chemistry*; D011487:Protein Conformation; D017433:Protein Structure, Secondary
chemical_list: D009842:Oligopeptides; D010455:Peptides; C011083:polyproline