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.