After processing raw files with the in house–developed software MaxQuant (version 1.0.12.36 or 1.0.13.12; Cox and Mann, 2008 (link)), data were searched against the human database concatenated with reversed copies of all sequences (Peng et al., 2003 (link)) and supplemented with frequently observed contaminants (porcine trypsin, achromobacter lyticus lysyl endopeptidase, and human keratins) using MASCOT (version 2.2.0; Matrix Science). For the analysis of pericentrin experiments, the mouse pericentrin sequence was added to the database. Carbamidomethylated cysteins were set as fixed, oxidation of methionine, and N-terminal acetylation as variable modification. Mass deviation of 0.5 D was set as maximum allowed for MS/MS peaks, and a maximum of two missed cleavages were allowed. Maximum false discovery rates (FDRs) were set to 0.01 both on peptide and protein levels. Minimum required peptide length was six amino acids.
Quantification of proteins in SILAC experiments was performed using MaxQuant (Cox and Mann, 2008 (link)). Methionine oxidations and acetylation of protein N termini were specified as variable modifications and carbamidomethylation as fixed modification. Maximum peptide charge was set to 6. SILAC settings were adjusted to doublets, and Lys0 and Lys8 were selected as light and heavy label, respectively. Peptide and protein FDRs were set to 0.01. The maximum PEP was set to 1, and six amino acids were required as minimum peptide length. Only proteins with at least two peptides (thereof one uniquely assignable to the respective protein group) were considered as reliably identified. Unique and razor peptides were considered for quantification with a minimum ratio count of 2. Forward and reverse experiments were analyzed together and specified as QUBICH and QUBICL in the experimentalDesign.txt. Ratios of the reverse experiment QUBICL were inverted. Specific interaction partners in SILAC experiments were determined by a combination of ratio and ratio significance calculated by MaxQuant. The p-value for the significance of enrichment had to be <0.01 in both the forward and reverse experiment. The provided R script QUBIC-SILAC.R was used to plot all identified proteins according to their ratios in the forward and reverse experiment and mark specific interaction partners (http://www.r-project.org ).
Label-free quantification was performed with MaxQuant (see Supplemental data). Methionine oxidations and acetylation of protein N termini were specified as variable modifications and carbamidomethylation as fixed modification. Maximum peptide charge was set to 6. SILAC settings were set to singlets. Peptide and protein FDRs were set to 0.01. The maximum PEP was set to 1, and six amino acids were required as minimum peptide length. Only proteins with at least two peptides (thereof one uniquely assignable to the respective protein group) were considered as reliably identified. Label-free protein quantification was switched on, and unique and razor peptides were considered for quantification with a minimum ratio count of 1. Retention times were recalibrated based on the built-in nonlinear time-rescaling algorithm. MS/MS identifications were transferred between LC-MS/MS runs with the “Match between runs” option in which the maximal retention time window was set to 2 min. The quantification is based on the extracted ion current and is taking the whole three-dimensional isotope pattern into account. At least two quantitation events were required for a quantifiable protein. Every single experiment/raw file was annotated as a separate experiment in experimentalDesign.txt. Control experiments were named Control1, Control2, and Control3. Pull-downs were named with the specific bait name and the replicate number. Identification of specific interaction partners was determined using the MaxQuant-based program QUBICvalidator. The proteinGroups.txt file was loaded (Load – Generic), and a group file template, Groups.txt, was generated (Processing – Groups – Write group file template). Replicates were grouped using one unique name in Groups.txt. The file was then loaded into QUBICvalidator (Processing – Groups – Load groups). Subsequently, results were cleaned for reverse hits and contaminants (Processing – Filter – Filter category – Reverse = + and Contaminant = +). Positive intensity values were logarithmized (Processing – Transformation – LOG – Log2). Signals that were originally zero were imputed with random numbers from a normal distribution, whose mean and standard deviation were chosen to best simulate low abundance values below the noise level (Processing – Imputation – Replace missing values by normal distribution – Width = 0.3; Shift = 1.8). Significant interactors were determined by a volcano plot-based strategy, combining t test p-values with ratio information. The standard equal group variance t test was applied (Processing – Testing – Two groups). Significance lines in the volcano plot corresponding to a given FDR were determined by a permutation-based method (Tusher et al., 2001 (link)). The pull-down was selected as Group1 and the control as Group2. Threshold values (= FDR) were selected between 0.1 and 0.001 and SO values (= curve bend) between 0.5 and 2.0. The resulting table was then exported (Export – Tab separated). The second tab (Table S1 and Table S2 ) was selected, and values saved with the same file name were supplemented with “_sup” (e.g., Exp.txt → Exp_sup.txt). Results were then plotted using the open source statistical software R and the provided script QUBIC-LABELFREE.R. In the beginning of the script, Exp.txt and Exp_sup.txt have to be replaced with the real file names. Dynamic experiments were plotted using the script QUBIC-LABELFREE_dynamic.R. Significant TREX and TACC3 interactors were clustered using Genesis (Sturn et al., 2002 (link)).
A detailed step by step protocol and the raw data and programs associated with this manuscript may be downloaded fromhttps://proteomecommons.org/tranche , launching Tranche, choosing “Open By Hash”, and entering the following hash: iNYsECWFuN0KDV0Q8QoE3uXxRGuBiCo5+iwydOM7h29jlyPv+Xv4+1piRkFr+mcnsy+eErYIvmcRQf9ZU/l5lxQYNQYAAAAAAABFCA==
Quantification of proteins in SILAC experiments was performed using MaxQuant (Cox and Mann, 2008 (link)). Methionine oxidations and acetylation of protein N termini were specified as variable modifications and carbamidomethylation as fixed modification. Maximum peptide charge was set to 6. SILAC settings were adjusted to doublets, and Lys0 and Lys8 were selected as light and heavy label, respectively. Peptide and protein FDRs were set to 0.01. The maximum PEP was set to 1, and six amino acids were required as minimum peptide length. Only proteins with at least two peptides (thereof one uniquely assignable to the respective protein group) were considered as reliably identified. Unique and razor peptides were considered for quantification with a minimum ratio count of 2. Forward and reverse experiments were analyzed together and specified as QUBICH and QUBICL in the experimentalDesign.txt. Ratios of the reverse experiment QUBICL were inverted. Specific interaction partners in SILAC experiments were determined by a combination of ratio and ratio significance calculated by MaxQuant. The p-value for the significance of enrichment had to be <0.01 in both the forward and reverse experiment. The provided R script QUBIC-SILAC.R was used to plot all identified proteins according to their ratios in the forward and reverse experiment and mark specific interaction partners (
Label-free quantification was performed with MaxQuant (see Supplemental data). Methionine oxidations and acetylation of protein N termini were specified as variable modifications and carbamidomethylation as fixed modification. Maximum peptide charge was set to 6. SILAC settings were set to singlets. Peptide and protein FDRs were set to 0.01. The maximum PEP was set to 1, and six amino acids were required as minimum peptide length. Only proteins with at least two peptides (thereof one uniquely assignable to the respective protein group) were considered as reliably identified. Label-free protein quantification was switched on, and unique and razor peptides were considered for quantification with a minimum ratio count of 1. Retention times were recalibrated based on the built-in nonlinear time-rescaling algorithm. MS/MS identifications were transferred between LC-MS/MS runs with the “Match between runs” option in which the maximal retention time window was set to 2 min. The quantification is based on the extracted ion current and is taking the whole three-dimensional isotope pattern into account. At least two quantitation events were required for a quantifiable protein. Every single experiment/raw file was annotated as a separate experiment in experimentalDesign.txt. Control experiments were named Control1, Control2, and Control3. Pull-downs were named with the specific bait name and the replicate number. Identification of specific interaction partners was determined using the MaxQuant-based program QUBICvalidator. The proteinGroups.txt file was loaded (Load – Generic), and a group file template, Groups.txt, was generated (Processing – Groups – Write group file template). Replicates were grouped using one unique name in Groups.txt. The file was then loaded into QUBICvalidator (Processing – Groups – Load groups). Subsequently, results were cleaned for reverse hits and contaminants (Processing – Filter – Filter category – Reverse = + and Contaminant = +). Positive intensity values were logarithmized (Processing – Transformation – LOG – Log2). Signals that were originally zero were imputed with random numbers from a normal distribution, whose mean and standard deviation were chosen to best simulate low abundance values below the noise level (Processing – Imputation – Replace missing values by normal distribution – Width = 0.3; Shift = 1.8). Significant interactors were determined by a volcano plot-based strategy, combining t test p-values with ratio information. The standard equal group variance t test was applied (Processing – Testing – Two groups). Significance lines in the volcano plot corresponding to a given FDR were determined by a permutation-based method (Tusher et al., 2001 (link)). The pull-down was selected as Group1 and the control as Group2. Threshold values (= FDR) were selected between 0.1 and 0.001 and SO values (= curve bend) between 0.5 and 2.0. The resulting table was then exported (Export – Tab separated). The second tab (
A detailed step by step protocol and the raw data and programs associated with this manuscript may be downloaded from