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.
Fariselli
P
P
Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy.
Riccobelli
P
P
Casadio
R
R
D001665:Binding Sites; D005075:Biological Evolution; D003545:Cysteine / Q000737:chemistry*; D016208:Databases, Factual; D004220:Disulfides / Q000737:chemistry; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*
D004220:Disulfides; D011506:Proteins; D003545:Cysteine