Predicting residue–residue contact maps by a two‐layer, integrated neural‐network method

Abstract

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 two‐layer neural network. SPINE‐2D achieved a 10‐fold cross‐validated accuracy of 47% (±2%) for top L /5 predicted contacts between two residues with sequence separation of six or more and an accuracy of 24 ± 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. SPINE‐2D is available as a webserver at http://sparks.informatics.iupui.edu. Proteins 2009.