DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification
DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved...
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| Veröffentlicht in: | Nucleic acids research 2006-07, Vol.34 (suppl-2), p.W182-W185 |
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| creator | Ferrè, F Clote, P |
| description | DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem--to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at: http://bioinformatics.bc.edu/clotelab/DiANNA/. |
| doi_str_mv | 10.1093/nar/gkl189 |
| format | Article |
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Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org</rights><rights>Copyright Oxford University Press(England) 2006</rights><rights>The Author 2006. Published by Oxford University Press. 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Acids Res</addtitle><description>DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem--to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. 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| source | Oxford Journals Open Access Collection; MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Artificial Intelligence Cysteine - chemistry Cysteine - classification Disulfides - chemistry Internet Oxidation-Reduction Proteins - chemistry Sequence Analysis, Protein Software User-Computer Interface |
| title | DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification |
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