Protein secondary structure prediction with dihedral angles
Copyright policy )Protein secondary structure prediction with dihedral angles
AbstractWe 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 ψ 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. Proteins 2005. © 2005 Wiley‐Liss, Inc.
- Nottingham Trent University United Kingdom
Models, Molecular, Caspase 3, Proteins, Reproducibility of Results, Caspases, Initiator, Peptide Fragments, Protein Structure, Secondary, Caspases, Humans, Amino Acid Sequence, Neural Networks, Computer
Models, Molecular, Caspase 3, Proteins, Reproducibility of Results, Caspases, Initiator, Peptide Fragments, Protein Structure, Secondary, Caspases, Humans, Amino Acid Sequence, Neural Networks, Computer
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