PuSH - Publikationsserver des Helmholtz Zentrums München

Kolbert, Z.* ; Lindermayr, C.

Computational prediction of NO-dependent posttranslational modifications in plants: Current status and perspectives.

Plant Physiol. Biochem. 167, 851-861 (2021)
Verlagsversion Postprint DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Computational Prediction ; Nitric Oxide ; Posttranslational Modification ; S-nitrosation ; Tyrosine Nitration; Protein-tyrosine Nitration; S-nitrosylation Sites; Nitric-oxide; Arabidopsis-thaliana; Glyceraldehyde-3-phosphate Dehydrogenase; Differential Inhibition; Proteomic Analysis; Mass-spectrometry; Identification; Peroxynitrite
ISSN (print) / ISBN 0981-9428
e-ISSN 1873-2690
Quellenangaben Band: 167, Heft: , Seiten: 851-861 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort 65 Rue Camille Desmoulins, Cs50083, 92442 Issy-les-moulineaux, France
Begutachtungsstatus Peer reviewed
Förderungen National Research, Development and Innovation Office