title: Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks.
abstract: A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 80% using neural networks based on evolutionary information. When the whole protein is taken into account by means of an HMM, a hybrid system is generated, whose emission probabilities are computed using the NN output (hidden neural networks). In this case, the predictor accuracy increases up to 88%. Further, when tested on a protein basis, the hybrid system can correctly predict 84% of the chains in the data set, with a gain of at least 27% over the NN predictor.
authors.affiliation: Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 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*; D004220:Disulfides / Q000737:chemistry*; D008390:Markov Chains; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*
chemical_list: D004220:Disulfides; D011506:Proteins; D003545:Cysteine