Protein secondary structure prediction with dihedral angles | Zendy

Wood Matthew J. | Zendy; Hirst Jonathan D. | Zendy

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Protein secondary structure prediction with dihedral angles

Author(s) -

Wood Matthew J.,

Hirst Jonathan D.

Publication year - 2005

Publication title -

proteins: structure, function, and bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.699

H-Index - 191

eISSN - 1097-0134

pISSN - 0887-3585

DOI - 10.1002/prot.20435

Subject(s) - dihedral angle , cascade , set (abstract data type) , protein secondary structure , algorithm , computer science , correlation , artificial intelligence , mathematics , pattern recognition (psychology) , chemistry , geometry , molecule , hydrogen bond , biochemistry , organic chemistry , chromatography , programming language

We present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three‐state accuracy (Q 3 ) 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.

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