S-nitroso- and nitro- proteomes in the olive (Olea europaea L.) pollen. Predictive versus experimental data by nano-LC-MS Carmona, Rosario Jiménez-Quesada, María José Lima-Cabello, Elena Traverso Gutiérrez, José Ángel Castro, Antonio Jesús Claros Díaz, Manuel Gonzalo Alché Ramírez, Juan de Dios Immunoprecipitation Proteome S-nitrosylation Transcriptome Tyrosine nitration This work is part of Ph.D. thesis by Maria Jose Jimenez-Quesada, and was supported by European Regional Development Fund (ERDF) co-funded projects BFU2011-22779, BFU2016-77243-P, RTC-2015-4181-2 and RTC-2016-4824-2 (Spanish MINECO), P2011-CVI-7487 (Junta de Andalucia) and 201540E065 (CSIC). 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. 2021-02-05T12:29:13Z 2021-02-05T12:29:13Z 2017-10-06 dataset R. Carmona et al. S-nitroso- and nitro- proteomes in the olive (Olea europaea L.) pollen. Predictive versus experimental data by nano-LC-MS. Data in Brief 15 (2017) 474–477 [https://doi.org/10.1016/j.dib.2017.09.058] http://hdl.handle.net/10481/66330 10.1016/j.dib.2017.09.058 10.30827/Digibug.66330 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Elsevier