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Prediction of the disulfide‐bonding state of cysteines in proteins at 88% accuracy
Author(s) -
Martelli Pier Luigi,
Fariselli Piero,
Malaguti Luca,
Casadio Rita
Publication year - 2002
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.0219602
Subject(s) - cysteine , hidden markov model , disulfide bond , markov chain , artificial neural network , computer science , task (project management) , pattern recognition (psychology) , protein structure prediction , artificial intelligence , biological system , chemistry , computational biology , protein structure , algorithm , machine learning , biochemistry , biology , engineering , systems engineering , enzyme
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.