Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and to filter out the worst performing ones. In this study, we optimised a robust LCMS1 shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios, 2 extraction buffers, 3 sets of proteases, and multiple LC settings. Protein identifications by LCMS2 was used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalized using sample weights, an internal standard, and Quality Controls. Data mining tools found particularly useful to explore the flour proteome are presented. DOI 10.3390/ijms23020713
[doi:10.25345/C5585Q]
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: Triticum aestivum ; shotgun proteomics ; LC-MS/MS, protease, normalisation, data mining
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Delphine Vincent, Agriculture Victoria Research, Australia |
Submitting User: | delphine_1_2 |
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