title: Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy.
abstract: The task of predicting the cysteine-bonding state in proteins starting from the residue chain is addressed by implementing a new hybrid system that combines a neural network and a hidden Markov model (hidden neural network). Training is performed using 4136 cysteine-containing segments extracted from 969 nonhomologous proteins of well-resolved three-dimensional structure. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 88% and 84%, when measured on cysteine and protein basis, respectively. These results outperform previously described methods for the same task.
authors.affiliation: Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, 40126 Bologna, Italy.
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Martelli
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
authors.forename: Luca
authors.initials: L
authors.lastname: Malaguti
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
mesh_terms: D003545:Cysteine / Q000737:chemistry*; D030562:Databases, Protein; D004220:Disulfides / Q000737:chemistry*; D008390:Markov Chains; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D004220:Disulfides; D011506:Proteins; D003545:Cysteine