Computational protocol: Quantitative Tandem Affinity Purification, an Effective Tool to Investigate Protein Complex Composition in Plant Hormone Signaling: Strigolactones in the Spotlight

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Protocol publication

[…] All raw files were processed with the MaxQuant software (version 1.4.1.2) (). The derived data were searched with the built-in Andromeda search engine against the Arabidopsis thaliana forward/reversed version of the TAIR10_pep_20101214 database containing also sequences of frequently observed contaminants, including human keratins, bovine serum proteins, or proteases. Carbamidomethylation of cysteines was selected as the fixed modification, whereas variable modifications were set to oxidation and acetylation (protein N-term). Trypsin∖P was selected as enzyme setting. Cleavage was allowed when arginine or lysine was followed by proline with two missed cleavages permitted. Matching between runs was enabled with a matching window time of 30 s. Relative, LFQ of proteins was selected by means of the MaxLFQ algorithm integrated into MaxQuant. With the minimum ratio count set to 1, the FastLFQ option was enabled, LFQ minimum number of neighbors was set to 3, and the LFQ average number of neighbors to 6, as per default. Proteins identified with at least one unique peptide were retained. The false discovery rate (FDR) for peptide and protein identifications was set to 1%, and the minimum peptide length was set to 7 amino acids. Detailed MaxQuant search parameters can be found in Supplementary Table . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE () partner repository with the dataset identifier PXD009083. [...] After MS data processing, LFQ values from the “proteinGroups.txt” output file of MaxQuant were further analyzed in the Perseus software (version 1.5.3.2). First, the reverse database hits, contaminants, and proteins identified only by modified peptides were filtered out. Then, log2 values were taken from the LFQ intensities, whereafter samples were grouped in ‘mock’ and ‘treatment.’ Proteins that did not contain at least four valid values in at least one group were filtered out and missing LFQ values were imputed/replaced by values from a normal distribution that were slightly lower than the lowest (log) value measured, as described (; ). All the imputed missing values can be found in the Supplementary Data Sheet . For normalization on the bait level, the intensity values from the “proteinGroups.txt” of the MaxQuant output file were analyzed in the same manner as the LFQ values. Before the imputation step, the SMXL7 intensity was subtracted from the intensity value of each protein. A Student’s t-test was applied to determine statistical outliers between ‘mock’ and ‘treatment’ groups. The resulting differences between the means of the two groups [“log2(mock/treatment”)] and the negative log10 P values were plotted against each other in volcano plots. The multiple hypothesis testing problem was corrected with a permutation based FDR (0.05). The threshold value S0 was set at 0.1 by default. […]

Pipeline specifications

Software tools MaxQuant, Andromeda, Perseus
Databases TAIR
Application MS-based untargeted proteomics
Organisms Arabidopsis thaliana