Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy.

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.