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Role of evolutionary information in predicting the disulfide‐bonding state of cysteine in proteins

A neural network‐based predictor is trained to distinguish the bonding states of cysteine in proteins starting from the residue chain. Training is performed by using 2,452 cysteine‐containing segments extracted from 641 nonhomologous proteins of well‐resolved three‐dimensional structure. After a cross‐validation procedure, efficiency of the prediction scores were as high as 72% when the predictor is trained by using protein single sequences. The addition of evolutionary information in the form of multiple sequence alignment and a jury of neural networks increases the prediction efficiency up to 81%. Assessment of the goodness of the prediction with a reliability index indicates that more than 60% of the predictions have an accuracy level greater than 90%. A comparison with a statistical method previously described and tested on the same database shows that the neural network‐based predictor is performing with the highest efficiency. Proteins 1999;36:340–346. © 1999 Wiley‐Liss, Inc.