title: Protein secondary structure prediction with dihedral angles.
abstract: We present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three-state accuracy (Q3) of 79.4% in a cross-validated trial on a nonredundant set of 513 proteins. An iterative set of cascade-correlation neural networks is used to predict both secondary structure and psi dihedral angles, with predicted values enhancing the subsequent iteration. Predictive accuracies of 80.7% and 81.7% are achieved on the CASP4 and CASP5 targets, respectively. Our approach is significantly more accurate than other contemporary methods, due to feedback and a novel combination of structural representations.
authors.affiliation: School of Chemistry, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Wood
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Hirst
mesh_terms: D000595:Amino Acid Sequence; D053148:Caspase 3; D020169:Caspases / Q000737:chemistry; D053471:Caspases, Initiator; D006801:Humans; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010446:Peptide Fragments / Q000737:chemistry; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D015203:Reproducibility of Results
chemical_list: D010446:Peptide Fragments; D011506:Proteins; C505407:CASP3 protein, human; C093516:CASP4 protein, human; D053148:Caspase 3; D020169:Caspases; D053471:Caspases, Initiator