title: 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.
authors.affiliation: Indiana University School of Informatics, Indiana University-Purdue University, Indianapolis, Indiana 46202, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Xue
authors.forename: Eshel
authors.initials: E
authors.lastname: Faraggi
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins