title: Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network.
abstract: A hidden neural network-based method is used to predict the bonding state of cysteines starting from the residue sequence of the protein chain. The method scores as high as 89% and 86% per cysteine residue and per protein, respectively, and in this overcomes other predictors of the same category. We then explore the efficacy of our predictor in computing the disulfide content of the whole proteome of Escherichia coli (K12 and O157), Aeropirum pernix, Thermotoga maritima, and Homo sapiens. We find that the percentage of extracellular disulfide containing proteins is higher than that of intracellular one, and that the human proteome is by far the one with the highest content of sulfur-sulfur linkages in proteins.
authors.affiliation: Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, Italy.
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Martelli
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
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; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*; D020543:Proteome; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein
chemical_list: D004220:Disulfides; D011506:Proteins; D020543:Proteome; D003545:Cysteine