Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system
read more
Citations
Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer
Doxorubicin generates senescent microglia that exhibit altered proteomes, higher levels of cytokine secretion, and a decreased ability to internalize amyloid β
Contribution of Mass Spectrometry-Based Proteomics to the Understanding of TNF-α Signaling.
Serum responsive proteome reveals correlation between oxidative phosphorylation and morphogenesis in Candida albicans ATCC10231
Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results
References
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.
Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
From molecular to modular cell biology.
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Related Papers (5)
Frequently Asked Questions (16)
Q2. What is the effect of phosphorylation of AKTS1 on mTORC?
It is thought that phosphorylation of AKTS1 by AKT induces 14-3-3 binding of AKTS1 leading to dissociation of AKTS1 from mTORC1 and subsequent reduction of mTORC1 inhibition.
Q3. Why did the authors choose to stimulate the 14-3-3 interactome?
The authors chose IGF1 stimulation to perturb the 14-3-3 interactome because prior data showed that substrates of AKT kinase, a central mediator of insulin-IGF1 signaling, frequently bind 14-3-3 proteins5.
Q4. How many unique proteins were identified across the entire experiment?
The authors characterized these samples by shotgun mass spectrometry leading to the identification of 31,509 unique peptide sequences (FDR 0.2 %) corresponding to 2,532 unique proteins (FDR 0.3%) across the entire experiment.
Q5. What was the way to estimate the false discovery rate?
the optimal separation between true and false peak groups was achieved using a linear model training and the score distribution from the shuffled decoy assays was used to estimate the false discovery rate (mProphet62).
Q6. What could be used to help define protein complexes in the cell?
Combination with orthogonal information, such as might be provided from improvements in native protein complex separation strategies47, or proximity labeling48, could prove instrumental toward this end.
Q7. How was the collision energy determined for each window?
The collision energy for each window was determined based on the calculation for a charge 2+ ion centered upon the window with a spread of 15.
Q8. What are the corresponding plots for GFP purifications?
The corresponding plot area for GFP purifications is essentially devoid of proteins other than the control bait itself (GFP), 2 zinc finger proteins (Q9H5H4 and O60290) and another DNA binding protein (Q15424) which are very consistently enriched in control purifications for unknown reasons.
Q9. How many peptide features were identified in the AP-SWATH dataset?
From the 14-3-3 AP-SWATH dataset the authors identified 19,123 peptide features (corresponding to 1,967 proteins) at 1% FDR in 3 out of 3 biological replicates from at least one experimental condition (Supplementary Fig. 4).
Q10. What was the correct python script to use to correct this?
As the precursor isolation window scheme was wrongly retrived by msconvert, a custom python script (fix_swath_windows.py) was used to correct this.
Q11. What did the authors do to characterize the dynamics of the 14-3-3 interactome?
Having used the AP-SWATH quantitative data to identify 567 high confidence 14-3-3β interacting proteins, the authors went on to characterize their dynamics.
Q12. What is the largest interactome for a single bait?
To their knowledge, this study represents the largest reported interactome for a single bait, and further indicates that at least 2.8 % of the proteome can be engaged by 14-3-3 dimers containing the 14-3-3β isoform.
Q13. what are the proteins in panels (a) – (h)?
The 5 selected proteins in panels (a) – (e) are typical of the 5 clusters found in panels (g) – (h). IGF1R shown in panel (f) is nominally part of cluster 2, however, the time course behavior is not completely consistent as IGF1R shows no response to the PI3K inhibitor unlike the other proteins shown in cluster 2. Panel (i) shows the enrichment of known or predicted AKT substrates in each of the 5 clusters compared to the background UniProt human proteome.
Q14. How did the authors determine the significance of the 14-3-3 interactor?
The authors found 14-3-3β as an interactor in 19 out of 21 kinases, whereas, no 14-3-3β was detected in APs from GFP or from 21 additional kinases selected as negative controls (Fig. 3b).
Q15. How many protein interactions were detected in the shotgun?
APs of log2 fold change > 2 and adjusted p-value < 0.01. (b) A subset of the detected protein interactions were verified by reciprocal AP-MS. 14-3-3β (red circles) was detected as a protein interaction in 19 of the 21 protein kinases analysed.
Q16. What was the python script used to split the mzXML files?
For easier file access, the mzXML files were split into 33 individual files (32 SWATH MS2 files + 1 MS1 file) using the custom python script (split_mzXML_intoSwath.py).