Predicting residue–residue contact maps by a two-layer, integrated neural-network method
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A neural network method (SPINE-2D) is introduced to provide a sequence-based prediction of residue-residue contact maps. This method is built on the success of SPINE in predicting secondary structure, residue solvent accessibility, and backbone torsion angles via large-scale training with overfit protection and a twolayer neural network. SPINE- 2D achieved a 10-fold crossvalidated accuracy of 47% (62%) for top L/5 predicted contacts between two residues with sequence separation of six or more and an accuracy of 24 6 1% for nonlocal contacts with sequence separation of 24 residues or more. The accuracies of 23% and 26% for nonlocal contact predictions are achieved for two independent datasets of 500 proteins and 82 CASP 7 targets, respectively. A comparison with other methods indicates that SPINE-2D is among the most accurate methods for contact- map prediction.
Proteins: Structure, Function, and Bioinformatics
© 2009 Wiley Periodicals, Inc. This is the accepted version of the following article: Predicting residue–residue contact maps by a two-layer, integrated neural-network method, Proteins: Structure, Function, and Bioinformatics, Vol. 76(1), 2009, pp. 176-183, which has been published in final form at dx.doi.org/10.1002/prot.22329.