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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.