1.2 Mass spectrometry-based quantitative proteomics
1.2.3 Interaction proteomics
standard has to be spiked in.
Although the intensity between two different peptides cannot be directly used to in-fer quantitative information, approaches were developed that can estimate absolute amounts without using isotope labeled spike in standards. The empirical abundance index (emPAI), for example, is computed as ten to the power of the number of observed peptides divided by the number of theoretical peptides minus one. Interestingly, the emPAI shows direct proportionality to the absolute protein amount [92].
A more refined method is to calculate a so-called iBAC (intensity-based absolute quan-tification) intensity [213]. In this method the intensities of all identified peptides of a protein are summed up, divided by the number of theoretically observed peptides and log transformed. To perform absolute quantification, a non-labeled standard of accu-rately quantified proteins is spiked into the sample before sample preparation. Using iBAC intensities for the standard proteins, the absolute protein amount of all identified proteins can be estimated using a linear regression [213].
label-free approaches because they were used in this thesis.
SILAC-based interaction proteomics
SILAC currently offers the best and most reliable quantification method for interaction proteomics. Proteins are already metabolically labeled before the pull-down, thus labe-ling artifacts or incomplete labelabe-ling, which can happen for chemical labelabe-ling approa-ches, are circumvented. The experimental design of SILAC-based interaction screens in general follows the same standard principles: In the forward experiment, the spe-cific pull-down is performed with the heavy and the control pull-down with the light labeled extracts. For the reverse experiment labels are swapped (Figure 1.2.7 A). Pull-downs are generally performed separately to avoid subunit exchange reactions [102].
Beads are pooled after washing and bound proteins are eluted together. Whether sam-ples are fractionated or not depends on the complexity of the sample. For specific elutions single MS runs are usually sufficient.
A)
Forward Experiment Reverse Experiment Light
label Heavy
label
Enrichment Control
Pool and analyze
by MS Pool and analyze
by MS
Intensity
m/z
Intensity
m/z Light
label Heavy label
Enrichment Control
B)
log(ratio forward)
log(ratio reverse) backgroundbinders
specifically enriched specifically
repelled
Figure 1.2.7: SILAC based interaction workflowsA) Overview of a typical SILAC forward and reverse experiment. B) Ratio-ratio plot of a SILAC interaction experiment. Background cloud in green, proteins specifically binding (blue) can be found in the lower right quadrant, proteins specifically repelled (orange) can be found in the upper left quadrant.
Nonspecific binders show a ratio of around one in both experiments, whereas specific outliers have a high ratio in the forward and a low ratio in the reverse experiment. For visualization, logarithmized ratios from the forward and the reverse experiment are
plotted in a so-called ratio-ratio plot (Figure 1.2.7 B). Specific outliers can be found in the lower right quadrant. In some cases, enrichment on both baits makes biological sense (e.g. when comparing modified and unmodified peptides as baits) and outliers can be found in the lower right and the upper left quadrants. Ideally the background cloud forming around 0 is compressed and dense, and outliers are clearly offset. In almost all cases a visual inspection of the data is sufficient to pinpoint the specific hits, however, if necessary a statistical significance value can provide a robust p-value [25].
In most SILAC interaction studies, two enrichment experiments are compared: a spe-cific pull-down and a control pull-down. For some questions, it is desirable to directly compare two samples to a control. Triple labeling approaches enable such an analysis of three different states. This allows, for example, the dissection of co-operative effects of recruiting modifications on histone tails. Enhanced binding of TFIID to H3K4me3 upon acetylation of K9 and K14 was discovered using a triple labeling strategy [252].
In another study, the composition of the interactome of APC (Adenomatous polypo-sis coli) and AXIN1, two important proteins in the Wnt pathway, were investigated in their native and stimulated state [86].
Label-free interaction proteomics
Label-free approaches were recently shown to yield comparable results to SILAC-based quantification for interaction studies [88]. Samples from specific pull-down and control are prepared and measured separately, which gives rise to a higher variability. This can be minimized by automation, either by using a robotic workstation [88] or by setting up interaction experiments on 96-well plates as applied in this thesis. Intensity-based label-free quantification was performed by the MaxQuant software platform by com-puting a label-free intensity at the protein level [136]. In contrast to a SILAC approach which is very intuitive to analyze, label-free approaches need a more complex statisti-cal analysis to define outliers. A modified t-test statistic [245] that takes reproducibility and fold change into account has proven to provide a good separation of outliers from background binding proteins [88]. An additional parameter (termed s0) is introduced, which puts more weight on the relative difference between the groups.
d(i) = ¯xI(i)−¯s(i)+sxU(i)
0
Although label-free approaches need more replicates and are more complex to analyze than SILAC experiments, they offer some advantages. First of all, any protein source can directly be used. This allows interaction experiments from cells, which are
compli-cated to label and from organisms which are otherwise not accessible for SILAC-based studies. Near unlimited multiplicity is another advantage of label-free approaches.
Whereas in SILAC a maximum of three samples can be directly compared, label-free approaches allow the comparison of any number of baits.
Full-length protein-protein interactions
Protein-protein interactions are usually studied by enriching the protein of interest from a cell or tissue extract and analyzing the co-purified proteins. Pull-downs can be performed using antibodies against endogenous proteins, or by expression of tagged proteins and purifying them with an antibody against the tag. For the latter, good an-tibodies are available and furthermore a generic pull-down setup can be applied, mak-ing it very convenient for large scale interaction studies. Usmak-ing FLAG-tagged protein over-expression, the protein interactions of 5,000 individually taggedDrosophila pro-teins were analyzed [75]. However, the addition of a tag to a protein can interfere with protein-protein interactions by occluding interaction surfaces or by preventing proper protein folding. Moreover, protein over-expression can lead to artifacts. For example, mislocalization of the bait protein into cellular compartments where it normally would not be present, can force unphysiological protein interactions.
Co-IPs using antibodies against endogenous proteins and isotype antibodies as control circumvent many of the abovementioned problems. Endogenous Co-IPs heavily rely on the quality of the antibodies. First of all, antibodies against the bait proteins need to be available. Second, they need to be highly specific as cross-reactions with other pro-teins would generate artifacts. Although Co-IPs of endogenous propro-teins are not easily streamlined, a large interaction screen in human cells was recently performed [143]. To overcome the problem of cross-reactivity, the QUICK approach (QUantitative Immuno precipitation Combined with a Knockdown) was developed [216]. By using a cell line in which the protein of interest is knocked down, the same antibody can be used for Co-IP and control. Proteins which cross-react with the antibody will be equally en-riched in both purifications, and thereby not lead to false positive interactors.
BAC TransgeneOmics [192] is a powerful method to generate cell lines with tagged pro-teins at near endogenous expression levels. BACs (bacterial artificial chromosomes), encoding the gene of interest including introns and the gene-specific promoter, are modified by recombineering to include a GFP-tag and an antibiotic resistance marker.
These modified BACs are transfected into cell lines where they stably integrate into the genome. As the whole genomic region including the endogenous promoter is used, the resulting tagged proteins are expressed at near endogenous level. Moreover, cell
cycle-dependent protein expression patterns as well as cell type-specific isoforms can be obtained. The GFP tag is a very versatile tool, as it can be used for immunofluores-cence and ChIP assays [192]. It is also an excellent tag for protein-protein interaction studies [242]. The combination of BAC TransgenOmics and quantitative mass spec-trometry, termed QUBIC (Quantitative BAC InteraCtomics), is a powerful approach in interaction proteomics [88, 89]. Due to the large interest in GFP as a purification tag, GFP nanotraps were developed recently [204]. These are engineered proteins based on a single chain antibody from Llama which show excellent binding affinities to GFP.
Due to its much smaller size, nanotraps do not generate as many peptides as normal antibodies which would interfere with the subsequent MS analysis.
Modification-dependent protein-protein interactions
Many protein interactions in a cell are not constitutive, but only take place after a spe-cific stimulus. One way to accomplish this in a cell is by making protein interactions dependent on post-translational modifications. Several protein domains binding to a partner protein in a modification-dependent manner have evolved. For example, SH2 domains specifically bind to phosphorylated tyrosines [179], bromo domains bind to acetylated lysines [47] and many binding domains for methylated lysines are described [237].
A Peptide pull-downs approach using modified and unmodified bait peptides to screen for modification-dependent protein-protein interactions is a robust method. Bait pep-tides are coupled to beads via a biotin moiety and incubated with protein extracts.
Quantitative mass spectrometry (e.g. SILAC) is used to separate background binders from modification-dependent protein interactions [212]. The quantitative read-out is absolutely crucial, as a large number of proteins will bind unspecifically to the un-structured peptide bait [251]. This workflow was successfully applied to study phos-photyrosine binders [79, 217] and readers of lysine trimethylation [252].