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Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines

โœ Scribed by Ceroni, Alessio ;Frasconi, Paolo ;Passerini, Andrea ;Vullo, Alessandro


Book ID
111615098
Publisher
Springer
Year
2003
Tongue
English
Weight
194 KB
Volume
35
Category
Article
ISSN
0922-5773

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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 cysteinecontaining segments extracted from 641 nonhomologous proteins of well-resolved three-dimensional structure. After a cros