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

Proteins. 1999 Aug 15;36(3):340-6.


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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Binding Sites
  • Biological Evolution
  • Cysteine / chemistry*
  • Databases, Factual
  • Disulfides / chemistry
  • Neural Networks, Computer
  • Proteins / chemistry*


  • Disulfides
  • Proteins
  • Cysteine