title: Prediction of backbone dihedral angles and protein secondary structure using support vector machines.
abstract: BACKGROUND
  The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.
  
  
  RESULTS
  We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.
  
  
  CONCLUSIONS
  We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.
authors.affiliation: School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk
authors.forename: Petros
authors.initials: P
authors.lastname: Kountouris
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Hirst
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins