title: Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.
abstract: BACKGROUND
  Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.
  
  
  RESULTS
  We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.
  
  
  CONCLUSIONS
  We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.
authors.affiliation: School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
authors.forename: Petros
authors.initials: P
authors.lastname: Kountouris
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Hirst
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment
chemical_list: D011506:Proteins