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.