Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

TLDR: The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation, establishing AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks.

Abstract: Protein complexes and protein interaction networks are essential mediators of most biological functions. Complexes supporting transient functions such as signal transduction processes are frequently subject to dynamic remodeling. Currently, the majority of studies on the composition of protein complexes are carried out by affinity purification and mass spectrometry (AP-MS) and present a static view of the system. For a better understanding of inherently dynamic biological processes, methods to reliably quantify temporal changes of protein interaction networks are essential. Here we used affinity purification combined with sequential window acquisition of all theoretical spectra (AP-SWATH) mass spectrometry to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks.

Full text

Quantifying protein interaction dynamics by SWATH mass
spectrometry: application to the 14-3-3 system
Collins, B. C., Gillet, L. C., Rosenberger, G., Röst, H. L., Vichalkovski, A., Gstaiger, M., & Aebersold, R. (2013).
Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system.
Nature Methods
,
10
(12), 1246-1253. https://doi.org/10.1038/nmeth.2703
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Nature Methods
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Peer reviewed version
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Download date:09. Aug. 2022

Quantifying protein interaction dynamics by SWATH mass
spectrometry: application to the 14-3-3 system
Ben C. Collins
1
, Ludovic C. Gillet
1
, George Rosenberger
1,2
, Hannes L. Röst
1,2
, Anton Vichalkovski
1
,
Matthias Gstaiger
1
, and Ruedi Aebersold
1,3,4
1
Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich,
Switzerland
2
Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, CH-8057 Zurich,
Switzerland
3
Competence Center for Systems Physiology and Metabolic Diseases, 8093 Zurich, Switzerland
4
Faculty of Science, University of Zurich, 8057 Zurich, Switzerland
Correspondence should be addressed to R. A. (aebersold@imsb.biol.ethz.ch)
Editorial Summary
Dynamic changes to the 14-3-3 protein interactome are robustly followed over
time using affinity purification-data independent analysis-based mass
spectrometry. Also online, Lambert et al. describe a similar method.

Abstract
Protein complexes and protein interaction networks are essential mediators of most biological
functions. Complexes supporting transient functions such as signal transduction processes are
frequently subject to dynamic remodeling. Currently, the majority of studies into the
composition of protein complexes are carried out by affinity purification and mass spectrometry
(AP-MS) and present a static view of the system. To better understand inherently dynamic
biological processes, methods to reliably quantify temporal changes of protein interaction
networks are essential. Here, we used affinity purification combined with SWATH mass
spectrometry (AP-SWATH) to study the dynamics of the 14-3-3β scaffold protein interactome
after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible
quantification of 1,967 proteins across all stimulation time points provided insights into the 14-
3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish
AP-SWATH as a tool to quantify dynamic changes in protein complex interaction networks.
Introduction
Cellular functions are rarely attributed solely to a single molecule. Typically, they are carried out
by sets of molecules organized into functional modules
1
. This is especially true in the case of
proteins associating to form functional multi-subunit protein complexes
2
. These range from
well-defined molecular machines to more dynamic structures including transient interactions
typical in signaling pathways. Scaffold, adaptor, or anchor proteins (generalized as scaffold
proteins) are of particular interest in signaling as they can mediate the spatial localization of
components in a cascade and take a central role in cellular processes as hubs controlling the
flow of information
3
. In a growing number of signaling systems, scaffold proteins are thought to
have essential functions such as facilitating assembly of functional protein complexes, causing
allosteric changes in ligand proteins, or affecting subcellular localization
4
. This apparently
central role of scaffold proteins makes them high value targets in protein interaction studies,
particularly for time-resolved measurements in perturbed systems where the reorganization of
protein complexes is common.
14-3-3 proteins are a family of 7 abundant cellular scaffolds which form homo- and
heterodimers with diverse regulatory functions in eukaryotes
5
. A 14-3-3 dimer can bind 2
phosphorylated serine or threonine residues situated on the same or different ligand proteins.
The phosphosites recognized by 14-3-3 proteins are in a sequence motif including an arginine in
the -3 or -4 position and a hydrophobic residue in the +2 position. The 14-3-3 ligand motif
therefore facilitates direct physical interactions but in many cases the ligand itself is complexed
to other proteins, and represents the attachment point to a multi-subunit complex. Therefore, in
APs of 14-3-3 proteins we expect to co-purify proteins that represent indirect 14-3-3
interactions. 14-3-3 proteins are thought to be among the most connected in the proteome with
respect to protein-protein interactions, with several hundred interactions reported per isoform
5
(previous 14-3-3 interaction studies
6-9
are described in Supplementary Note 1).
Phosphorylation of 14-3-3 ligand proteins is associated with activity of basophilic kinases, which
have consensus substrate motifs related to the 14-3-3 ligand motif. Insulin-IGF1 signaling has
been shown to be directly linked with the 14-3-3 system, and several AKT kinase substrates are
known to bind 14-3-3
5
, as shown in studies which compared the proteins interacting with 14-3-
3 in on and off states of this pathway
10-12
.

AP-MS (affinity purification - mass spectrometry) represents the method of choice to chart
protein-protein interactions under near physiological conditions
13,14
. Proteome-wide interaction
maps have been produced in model systems
15-17
, and many human protein interaction modules
have been described with great detail
18-20
and robustness
21
. While extensive information on
protein-protein connectivity is a valuable asset, results from these studies describe a static
representation of interaction networks only. There is an increasing awareness that to move
toward a more complete understanding of inherently dynamic biological processes it is essential
to generate time-resolved, quantitative descriptions of protein interaction networks in
perturbed states
22
. Until now this problem has been approached chiefly using MS1 intensity-
based quantification from AP-MS analysis of perturbed cell systems
10,23-26
. Such studies have
enjoyed a good degree of success, but remain hampered due to the technical limitations of
quantitative MS1 intensity-based and shotgun proteomics approaches
27
relating to data
completeness, linear dynamic range, and sensitivity. As such, the number of AP-MS studies
which have attempted to detect changes in protein-protein interaction networks in perturbed
systems remains small, at least with respect to projects engaged in large scale interaction
mapping
13,14,22
. A recent development has seen the application of targeted proteomics via SRM
(selected reaction monitoring) to differential interaction proteomics in an attempt to address
some of these limitations
28,29
. Termed AP-SRM, this approach succeeded in providing
quantitatively complete and sensitive measurements of a perturbed scaffold-centric protein
interaction network (see Supplementary Note 1).
Classical shotgun proteomics relies on DDA (data dependent acquisition), i.e. selection in real
time of precursors for fragmentation. As DDA is a semi-stochastic process the set of peptides
identified across samples is not reproducible as long as the number of detected precursors
exceeds the number of available sequencing cycles
27
. This is in contrast to SRM which
reproducibly targets a predetermined peptide set for quantification. DIA (data independent
acquisition) collects MS2 spectra for the entire expected mass range of tryptic peptides by co-
isolating and fragmenting multiple peptide precursors in isolation windows ranging from a few
m/z to the entire mass range simultaneously
30-34
. SWATH-MS
35
is a recently described
implementation of DIA which cycles through fixed precursor isolation windows (e.g. 25 m/z)
using a quadrupole-time-of-flight mass spectrometer achieving essentially complete peptide
fragment ion coverage for precursors in the tryptic peptides mass range. An essential feature
that distinguishes SWATH-MS from other DIA strategies is the use of prior knowledge regarding
fragmentation and chromatographic behavior of target peptides. This information is used for
scoring signal groups extracted from SWATH-MS datasets to identify and quantify peptides
automatically and at large scale
36
(unpublished data H. R., G. R.; www.openswath.org). SWATH-
MS provides SRM-like performance in terms of quantitative accuracy, data completeness and
dynamic range without specifying target peptides prior to data acquisition. Further, and unlike
SRM, SWATH-MS can quantify an unlimited number of target peptides as long as they have been
previously observed by shotgun MS.
We speculated that SWATH-MS could provide solutions to issues impeding progress in
quantitative interaction proteomics. To assess the capability of AP combined with SWATH-MS
(AP-SWATH) to rapidly and reliably identify and consistently quantify protein-protein
interactions in time-resolved perturbation experiments, we chose to study the dynamics of 14-3-
3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway.

Results
Characterization of the 14-3-3β signaling system
We selected 14-3-3 as a challenging system in which to evaluate the capability of AP-SWATH to
consistently quantify large numbers of protein-protein interactions across multiple conditions
and generated a stable HEK293 cell line inducibly expressing SH tagged 14-3-3β as described
18
(Supplementary Fig. 1). We 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 proteins
5
. As serum starvation was not sufficient to abolish AKT
activation, we included a 60 minute pretreatment with the reversible PI3K inhibitor LY294002
to achieve a PI3K inactive ground state. The time course consisted of 6 conditions in biological
triplicate (no treatment, -60 min; after LY294002 pre-treatment, 0 min; and 4 time points after
IGF1 stimulation, 1 min, 10 min; 30 min, and 100 min – see Fig. 1a). We confirmed activation of
the insulin-IGF1 pathway by Western blotting for an activating phosphorylation on AKT
(Supplementary Fig. 1a). Having established the 14-3-3β expression system, in which the bait
was expressed ~3-4 times below the endogenous level (Supplementary Fig. 1b), and confirmed
that the stimulation was effective, we performed APs of 14-3-3β from the time course. We
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. These data served as the basis for generating reference
spectra to quantify target peptides from AP-SWATH datasets.
Generation and analysis of AP-SWATH maps
The 18 affinity purified 14-3-3β samples from 6 conditions in the time course (Fig. 1), plus 9
GFP control APs, were subjected to SWATH-MS (Online Methods). The targeted data analysis
paradigm employed to identify and quantify peptides from SWATH-MS data is based on prior
knowledge of the fragmentation and chromatographic properties of each peptide. We used the
shotgun MS data to construct a consensus peptide MS/MS spectral library
37
(Fig. 1b) containing
31,469 sequence unique peptides and 41,934 MS2 consensus spectra including charge state and
modifications. We selected the 5 most intense fragments for each peptide precursor ion
resulting in 37,867 target assays containing 189,335 fragment ions for extraction of the AP-
SWATH dataset (Supplementary Table 1).
We analysed the AP-SWATH data using OpenSWATH, an open source software for automated
targeted analysis of DIA datasets (unpublished data H. R., G. R.; www.openswath.org). The
targeted analysis workflow consists of extracting ion chromatograms for groups of fragment
ions from a given peptide precursor in the appropriate MS2 SWATH map, and scoring peak
groups detected in these chromatograms with respect to prior knowledge from peptide MS2
spectral libraries (Supplementary Fig. 2 and 3 and Online Methods). From the 14-3-3 AP-
SWATH dataset we 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). The result was an essentially complete quantitative data matrix, displayed as a heat map
(Fig. 2 – source data is provided in the supplementary material), for all 1,967 proteins in all
conditions (see Supplementary Results for information on quantitative reproducibility). This
data matrix formed the basis for downstream quantitative comparisons relating firstly to
filtering of non-specific contaminants and identifying high confidence 14-3-3β interactions, and
secondly for examining the dynamics of 14-3-3β interacting proteins over the perturbation.

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Quantifying protein interaction dynamics by swath mass spectrometry: application to the 14-3-3 system" ?

Here, the authors used affinity purification combined with SWATH mass spectrometry ( AP-SWATH ) to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 143-3β interactome and its dynamic changes following IGF1 stimulation. Therefore, in APs of 14-3-3 proteins the authors expect to co-purify proteins that represent indirect 14-3-3 interactions. 14-3-3 proteins are thought to be among the most connected in the proteome with respect to protein-protein interactions, with several hundred interactions reported per isoform5 ( previous 14-3-3 interaction studies6-9 are described in Supplementary Note 1 ). As such, the number of AP-MS studies which have attempted to detect changes in protein-protein interaction networks in perturbed systems remains small, at least with respect to projects engaged in large scale interaction mapping13,14,22. Termed AP-SRM, this approach succeeded in providing quantitatively complete and sensitive measurements of a perturbed scaffold-centric protein interaction network ( see Supplementary Note 1 ). The authors speculated that SWATH-MS could provide solutions to issues impeding progress in quantitative interaction proteomics. To assess the capability of AP combined with SWATH-MS ( AP-SWATH ) to rapidly and reliably identify and consistently quantify protein-protein interactions in time-resolved perturbation experiments, the authors chose to study the dynamics of 14-33β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The authors selected 14-3-3 as a challenging system in which to evaluate the capability of AP-SWATH to consistently quantify large numbers of protein-protein interactions across multiple conditions and generated a stable HEK293 cell line inducibly expressing SH tagged 14-3-3β as described18 ( Supplementary Fig. 1 ). 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. Having established the 14-3-3β expression system, in which the bait was expressed ~3-4 times below the endogenous level ( Supplementary Fig. 1b ), and confirmed that the stimulation was effective, the authors performed APs of 14-3-3β from the time course. The authors selected the 5 most intense fragments for each peptide precursor ion resulting in 37,867 target assays containing 189,335 fragment ions for extraction of the APSWATH dataset ( Supplementary Table 1 ). The result was an essentially complete quantitative data matrix, displayed as a heat map ( Fig. 2 – source data is provided in the supplementary material ), for all 1,967 proteins in all conditions ( see Supplementary Results for information on quantitative reproducibility ). To determine which proteins were specific 14-3-3β interactors the authors examined the quantitative AP-SWATH data for enrichment of proteins in 14-3-3β To assess the validity of 14-3-3β protein interactions identified by AP-SWATH, the authors performed reciprocal purifications on a subset of proteins. The authors selected 21 protein kinases spanning the majority of the abundance range ( Supplementary Table 3 ), isolated the respective complexes by AP and analyzed the samples by shotgun MS. As direct interaction partners of 14-3-3 proteins generally contain a 14-3-3 ligand motif, the authors determined if it was overrepresented in the 14-3-3β interactome set. 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 143-3β isoform. To further verify these MS derived quantitative patterns the authors performed Western blotting on 6 selected 14-3-3β interactors. Given the dynamics of AKTS1, the authors chose to examine the behavior of other mTOR complex components41 present as 14-3-3β interactions. The authors retrieved the subset of 14-3-3β interacting proteins directly connected to mTOR kinase by previously described literature interactions ( Fig. 5a ). The authors extracted the quantitative profiles of these proteins over the perturbation ( Fig. 5b ). The phosphopeptide supporting the previously reported direct RCTOR-14-3-3β interaction42 was not detected. The authors created a schematic representation of the mTORC complexes as described in the literature39,41,42, overlaid with information from the AP-SWATH data in unstimulated and IGF1 stimulated states ( Fig. 5e ). Here, the authors show mTORC2 as essentially unchanged with respect to the stimulation, while AKTS1 binding to 14-3-3β is dramatically increased, consistent with the known model of AKTS1 phosphorylation and sequestration to 14-3-3 leading to relief of mTORC1 inhibition39. The method provides quantitative data for confidently identifying true protein-protein interactions via comparisons with control APs, and notably, for following dynamic changes in protein-protein interactions in perturbed systems. The authors demonstrated the advantages in characterizing highly complex AP samples from a signaling related scaffold protein where the range of detected interacting protein abundances spans more than 4 orders of magnitude, and where ~3 % of the proteome is specifically co-purified. Since the 14-3-3β interactome represents one of the most complex interactomes known, centered around a single hub protein, the authors expect AP-SWATH to have general applicability in many quantitative protein interaction projects. Indeed, this is demonstrated in the accompanying article from Lambert et al. 46 who use AP-SWATH to quantify changes in protein-protein interactions associated with sequence variants or following drug treatment. The dynamic interactome data allowed us to resolve distinct clusters, some of which are highly enriched in known or predicted substrates of AKT, and follow closely the profile of AKT activation. The authors also demonstrated the use of time-resolved quantitative AP-SWATH data in combination with known literature information to distinguish between the behavior of shared subunits from distinct protein complexes ( mTORC1/2 ). With that said, the authors would suggest that time-resolved protein abundance data from AP samples alone is unlikely to provide sufficient constraints to unambiguously define protein complexes in the cell and that analysis by large-scale reciprocal purification might not be feasible. This type of iterative reanalysis is unique to SWATH or DIA data and provides a compelling argument for further development in this area. The authors thank P. Navarro for support with data analysis and spectral library generation ; C. Ludwig, N. Selevsek, and Y. Liu for helpful discussions on SWATH-MS ; O. Schubert for the MTB SWATHMS data file for target assay comparison ; S. Hauri, A. van Drogen for advice on cell line generation, stimulation, and APs ; A. Kahraman for discussions on structural aspects of 14-3-3 ; V. Chang for support with SRMstats ; and the PRIDE team for assistance with upload of associated data. The authors gratefully acknowledge financial support from the SystemsX. ch project PhosphonetX and European Research Council advanced grant Proteomics v3. 0 ( grant 233226 ) from the European Union. L. G. co-developed the SWATH-MS methodology and provided critical input on analytical strategy. G. R. and H. R. developed the OpenSWATH software and provided critical input on SWATH-MS data analysis strategies. M. G. and R. A. conceived and co-supervised the project. B. C. wrote the manuscript with input from all authors. The authors declare no competing financial interests References 1. Figure Legends Figure 1 – AP-SWATH workflow schematic ( a ) 14-3-3β is affinity purified under native conditions over a time course which includes the following conditions: no treatment, t = -60 min ; after PI3K inhibitor pre-treatment, t = 0 min ; and 4 time points after IGF1 stimulation, t = 1 min, t = 10 min ; t = 30 min, and t = 100 min. The SWATH targeted data analysis was carried out using OpenSWATH r10367 ( Röst et al ; submitted manuscript ; for a tutorial and test data set see www. openswath. org ) running on an internal computing cluster and consisted of the following steps. However, in this case the authors did not perform automated quantification for all detected phosphopeptides due to the potential for errors from mismatching isobaric phosphopeptides which frequently share a majority of fragment ions. The authors used a non-strict filter on the quantitative data to remove proteins which showed no change ( -0. 5 > log2 FC > 0. 5, adjusted pvalue < 0. 05 at any time point compared with t=0 minutes ), and then performed time series clustering using the open source R package Mfuzz67 using median fold change as input. Further, and unlike SRM, SWATH-MS can quantify an unlimited number of target peptides as long as they have been previously observed by shotgun MS. The authors filtered the dataset at a significance level of adjusted p-value < 0. 01, and log2 FC > 2 to produce a high confidence 14-3-3β interactome containing 567 proteins ( Supplementary Table 2 ). Enrichment of 14-3-3 ligand motifs did not return to the baseline value until protein ~800-900 in the rank, suggesting there are ~20-30 % additional 14-3-3β interacting proteins which did not satisfy statistical significance, and could not be called as high confidence interactions. This, combined with the absence of mTORC1 specific subunits and radically different time course profiles of AKTS1 and mTOR suggests that the bulk of mTOR kinase in the 14-3-3β The dynamic data serves to highlight the separate behaviors of mTORC1 and mTORC2 leading to suggestions of how protein complexes are reorganized ( Fig. 5e ), as opposed to a static protein interaction network representation ( Fig. 5a ). This analysis suggests that their method is extremely sensitive in detecting interactions, particularly in highly complex scaffold protein APs. Finally, as APSWATH data contains an MS2 record for essentially the entire tryptic peptide space, there is potential for re-mining of the data post-acquisition using additional spectral libraries from sources such as synthetic peptides35,49, deep shotgun characterization, or enrichment of PTM containing peptides ( Supplementary Results on phosphopeptide analysis ). The authors believe that their APSWATH method could facilitate a shift towards more dynamic analyses in interaction proteomics22, with the eventual goal of answering questions relating to what degree of cellular environment sensing and functional control is mediated chiefly by reorganization of protein complexes and interaction networks. Dynamics for 6 selected 14-3-3β interactors was verified by Western blotting ( antibody dilutions: AKTS1 – 1:1000, TBCD4 – 1:100, CISY – 1:250, FBSP1 – 1:200, F262 – 1:100, RAE1L 1:100, HA – 1:5,000, see Supplementary Figure 7 for further antibody details ). Spectral library and target assay construction Profile mode wiff files from shotgun data acquisition were centroided and converted to mzML using the AB Sciex Data Converter v1. 3, and further converted to mzXML using ProteoWizard55 MSConvert v3. These files were then converted to profile mzML and gzipped for use in further analysis. Data analysis and statistics Peptide features ( i. e. peptides in a given charge state ) which met the 1 % FDR threshold in 3 out of 3 biological replicates for any experimental condition were retained, and intensities for these peptides/transitions across the experiment were used for further analysis. 

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).