S-nitroso- and nitro- proteomes in the olive (Olea europaea L.) pollen. Predictive versus experimental data by nano-LC-MS
The data presented here are related to the research article entitled “Generation of nitric oxide by olive (Olea europaea L.) pollen during in vitro germination and assessment of the S-nitroso- and nitro-proteomes by computational predictive methods” doi:10.1016/j.niox.2017.06.005 (Jimenez-Quesada et...
| Autores: | , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/164846 |
| Acceso en línea: | http://hdl.handle.net/10261/164846 |
| Access Level: | acceso abierto |
| Palabra clave: | S-nitrosylation Proteome Tyrosine nitration Transcriptomes Immunoprecipitation |
| Sumario: | The data presented here are related to the research article entitled “Generation of nitric oxide by olive (Olea europaea L.) pollen during in vitro germination and assessment of the S-nitroso- and nitro-proteomes by computational predictive methods” doi:10.1016/j.niox.2017.06.005 (Jimenez-Quesada et al., 2017) [1]. Predicted cysteine S-nitrosylation and Tyr-nitration sites in proteins derived from a de novo assembled and annotated pollen transcriptome from olive tree (Olea europaea L.) were obtained after using well-established predictive tools in silico. Predictions were performed using both default and highly restrictive thresholds. Numerous gene products identified with these characteristics are listed here. An experimental validation of the data, consisting in nano-LC-MS (Liquid Chromatography-Mass Spectrometry) determination of olive pollen proteins after immunoprecipitation with antibodies to anti-S-nitrosoCys and anti-3-NT (NitroTyrosine) allowed identification of numerous proteins subjected to these two post-translational modifications, which are listed here together with information regarding their cross-presence among the predictions. |
|---|