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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1016/j.plaphy.2021.09.011
|2 doi
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|a pubmed24n1102.xml
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|a (DE-627)NLM330788272
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|a (NLM)34536898
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|a (PII)S0981-9428(21)00475-7
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Kolbert, Zsuzsanna
|e verfasserin
|4 aut
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|a Computational prediction of NO-dependent posttranslational modifications in plants
|b Current status and perspectives
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 13.10.2021
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|a Date Revised 13.10.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2021 The Authors. Published by Elsevier Masson SAS.. All rights reserved.
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|a The perception and transduction of nitric oxide (NO) signal is achieved by NO-dependent posttranslational modifications (PTMs) among which S-nitrosation and tyrosine nitration has biological significance. In plants, 100-1000 S-nitrosated and tyrosine nitrated proteins have been identified so far by mass spectrometry. The determination of NO-modified protein targets/amino acid residues is often methodologically challenging. In the past decade, the growing demand for the knowledge of S-nitrosated or tyrosine nitrated sites has motivated the introduction of bioinformatics tools. For predicting S-nitrosation seven computational tools have been developed (GPS-SNO, SNOSite, iSNO-PseACC, iSNO-AAPAir, PSNO, PreSNO, RecSNO). Four predictors have been developed for indicating tyrosine nitration sites (GPS-YNO2, iNitro-Tyr, PredNTS, iNitroY-Deep), and one tool (DeepNitro) predicts both NO-dependent PTMs. The advantage of these computational tools is the fast provision of large amount of information. In this review, the available software tools have been tested on plant proteins in which S-nitrosated or tyrosine nitrated sites have been experimentally identified. The predictors showed distinct performance and there were differences from the experimental results partly due to the fact that the three-dimensional protein structure is not taken into account by the computational tools. Nevertheless, the predictors excellently establish experiments, and it is suggested to apply all available tools on target proteins and compare their results. In the future, computational prediction must be developed further to improve the precision with which S-nitrosation/tyrosine nitration-sites are identified
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|a Journal Article
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|a Review
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|a Computational prediction
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|a Nitric oxide
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|a Posttranslational modification
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|a S-Nitrosation
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|a Tyrosine nitration
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|a Amino Acids
|2 NLM
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|a Plant Proteins
|2 NLM
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|a Nitric Oxide
|2 NLM
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| 650 |
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|a 31C4KY9ESH
|2 NLM
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| 650 |
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|a Tyrosine
|2 NLM
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|a 42HK56048U
|2 NLM
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| 700 |
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|a Lindermayr, Christian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Plant physiology and biochemistry : PPB
|d 1991
|g 167(2021) vom: 15. Okt., Seite 851-861
|w (DE-627)NLM098178261
|x 1873-2690
|7 nnns
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|g volume:167
|g year:2021
|g day:15
|g month:10
|g pages:851-861
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|u http://dx.doi.org/10.1016/j.plaphy.2021.09.011
|3 Volltext
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