This work was supported by grants from the Wellcome Trust and BBSRC to IG. IG is a Wellcome Senior Fellow.

1. Generation and Passaging of SILAC-labeled Cell Lines
Note: the use of Trypsin-EDTA must be avoided at all stages of passaging and preparation of experimental samples for analysis as the trypsin may contain unlabeled amino acids, which would lead to incomplete labeling of samples.
<ol>
	<li>To prepare a bottle of SILAC media add a 0.5 ml aliquot of an appropriately SILAC-labeled arginine (84 mg/ml in PBS) and lysine (146 mg/ml in PBS) to a 500 ml bottle of Arg/Lys free DMEM (containing L-glutamine).</li>
	<li>Next, add 50 ml of dialyzed FBS and 5 ml of penicillin/streptomycin to the 500 ml bottle. 
	Note: HEK 293T cells (ATCC) should be maintained in DMEM media lacking arginine and lysine and supplemented with light (R0K0), medium (R6K4) or heavy (R10K8) amino acids, dialyzed fetal bovine serum (10 kDa cutoff), and penicillin/streptomycin. Cells must be maintained in media for a minimum of 5 cell divisions to ensure complete labeling. In most cases cells are readily labeled in &#8805;2 weeks.</li>
	<li>To split cells for passaging, cells should be dislodged from the monolayer by hitting the flask. Alternatives include the use of cell scrapers or by using enzyme-free, PBS-based cell-dissociation buffer. 
	Note: To conserve media, cells should be passaged in T25 flasks and larger cell numbers generated only immediately prior to an experiment.</li>
	<li>24 hours prior to transfecting cells, seed 3.5 x 106 SILAC-labeled cells into a single 10 cm2 dish for each experimental condition under investigation.</li>
</ol>
2. Transfection of Labeled Cells with pEGFP-fusion Constructs
<ol>
	<li>Remove the media from the cells and replace it with 9 ml of antibiotic-free SILAC DMEM (Light, Medium or Heavy) media. 
	Note: Antibiotics should not be added to the media since they may interfere with the efficiency of liposome-based transfection reagents.</li>
	<li>Prepare a mix of 10 &#956;g of the appropriate plasmid (epEGFP-C1, pEGFP-eIF4A-I, pEGFP-eIF4A-II) in 500 &#956;l of antibiotic-free SILAC DMEM and mix it with 500 &#956;l of antibiotic-free SILAC DMEM containing 10 &#956;l of transfection reagent (e.g., lipofectamine 2000). Mix the reaction thoroughly by pipetting it up and down several times.</li>
	<li>Incubate the reaction mix at room temperature for 20 min and then add drop-wise to the cell monolayer. Rock the plate gently from side to side.</li>
	<li>Incubate the transfected cells at 37 &#176;C and 10% CO2 for 24 hr. 
	Note: If the expression of a protein of interest results in any apparent toxicity, then it may be necessary to reduce the amount of plasmid transfected and/or the duration of the subsequent expression period.</li>
</ol>
3. Harvesting Cell Lysates
<ol>
	<li>Harvest cells from the dish into ice cold PBS using a cell scraper. Collect cells by centrifugation at 220 x g, 4 &#176;C for 5 min. Wash the cells a further 3x in 10 ml of ice-cold PBS.</li>
	<li>Resuspend the cell pellet in 200 &#956;l of cell lysis buffer (10 mM TrisCl/pH 7.5, 150 mM NaCl, 0.5 mM EDTA, 0.5% NP40) containing freshly added protease inhibitor cocktail III at 1x concentration and RNase cocktail (optional) at 5 &#956;l per ml. 
	Note: For proteins with known nucleic acid binding activity, it may be necessary to add nucleases to the lysate prior to precipitation. In cases where nuclease is added, samples should be incubated on ice for 30 min on ice, with pipetting every 10 min. Extracting total nucleic acid from a small fraction of the sample and analysis by agarose gel electrophoresis can test the effectiveness of the nuclease.</li>
	<li>Centrifuge samples at 13,000 x g, 4 &#176;C for 10 min retain the supernatant as the soluble cell lysate.</li>
	<li>The concentration of the cell lysate should be assessed by BCA assay according to the manufacturer&#8217;s instructions.</li>
	<li>Use lysis buffer containing protease inhibitor cocktail III to normalize protein concentration in a final volume of 500 &#956;l.</li>
	<li>Adjust the volume to 1 ml with the addition of 500 &#956;l of dilution buffer (10 mM TrisCl/pH 7.5, 150 mM NaCl, 0.5 mM EDTA) containing protease inhibitor cocktail III at final 1x concentration. A 50 &#956;l aliquot of the sample should be retained as the sample input and the lysate kept on ice whilst preparing anti-GFP beads (e.g., GFP-trap). 
	Note: Typically yields vary between 1-3.5 mg of protein in the final 1 ml sample. Whilst the above buffers are suitable for many proteins of interest, for others it may be necessary to modify buffer components to ensure the bait protein is solubilized and to maintain protein-protein interactions. Possible alterations include the buffering agent (Phosphate, HEPES), salt concentration (150-500 mM), the choice of detergent, or other additives.</li>
</ol>
4. Binding to Anti-GFP Beads
<ol>
	<li>Briefly vortex the bead slurry to resuspend the beads. Using a 200 &#956;l pipette tip with the end cut off, transfer 25 &#956;l of beads per sample to a fresh tube. 
	Note: The user should prepare the beads for a single SILAC experiment as a mastermix to minimize sample-to-sample variation.</li>
	<li>For each 25 &#956;l of bead slurry, add 20 volumes (1,500 &#956;l per 75 &#956;l slurry) of dilution buffer, and centrifuge the beads at 2,700 x g for 5 min. Next, wash the beads a further 2x in 20 volumes of dilution buffer.</li>
	<li>Add 100 &#956;l dilution buffer per 25 &#956;l bead slurry. Using a 200 &#956;l tip with the end cut off, transfer 85 &#956;l of this resuspended slurry to each of the SILAC-labeled samples from step 3.6.</li>
	<li>Incubate the samples with beads on a rotator at 4 &#176;C for 2 hr.</li>
</ol>
5. Washing, Elution, and Preparation of Samples for MS Analysis
<ol>
	<li>Centrifuge samples at 2,700 x g, 4 &#176;C for 5 min. 50 &#956;l of the supernatant should be retained as the unbound sample, with the remainder of the supernatant discarded.</li>
	<li>1ml dilution buffer should be added to each tube to resuspend the beads and the sample centrifuged at 2,700 x g, 4 &#176;C for 5 min. The supernatant should be discarded. This should be performed twice.</li>
	<li>Elute protein from the beads by the addition of 50 &#956;l of 2x SDS loading buffer, and heating at 95 &#176;C for 10 min. Pellet the beads by centrifugation at 2,700 x g for 2 min at 4 &#176;C</li>
	<li>Retain the supernatant in prelubricated tubes, where it can be then stored at -80 &#176;C until required. For submission to a mass spectrometry facility, mix labeled samples 1:1:1 (e.g., 10 &#956;l of each) and submit the mixed sample. 
	Note: At this point samples may be tested by western blot, silver-staining or other means to test for interaction with known/unknown binding partners. An example is given in Figure 2 showing specific binding of a known interaction partner &#8211; eIF4G by western blot, and the appearance of silver staining bands present in pulldown samples from a protein of interest, but not a control sample.</li>
	<li>Submit sample for LC-MS/MS analysis. 
	Note: once satisfied that a tagged protein of interest is successfully binding interaction partners, equal volumes of each labeled sample (light, medium and heavy) are combined and submitted for LC-MS/MS analysis. It is usual to submit 30 &#956;l total of an IP sample for analysis. This would entail mixing 10 &#956;l of Light-labeled sample with 10 &#956;l of medium-labeled and 10 &#956;l heavy-labeled samples to give a 30 &#956;l total.</li>
</ol>
6. Data Analysis I: Understanding the Results, and Removal of Low-confidence Identifications
Note: A list of the column headings returned by the Proteome Discoverer software is given in Table 1. Different software (e.g., MSQuant, MaxQuant) will return different headings, however, only a subset of these are required for analysis and these are common to the various software packages. Data should always include an accession number for each protein identified, ratio&#8217;s comparing each sample ratio (light vs medium, light vs heavy, medium vs heavy etc), the number of unique peptides identified, and some form of false positive rate or confidence indication.
<ol>
	<li>Before going through the data, first copy the raw data to a new spreadsheet. From this spreadsheet remove all the columns except those giving the accession number, number of unique peptides, ratios comparing samples, ratio variability, and the protein description. If replica experiments were performed, these should be combined into a single Excel file, with each experiment appearing on a separate tab. 
	Note: Low confidence data includes proteins identified by only a single unique peptide, and those where quantification was not possible.</li>
	<li>Use Excels&#8217; &#8216;sort&#8217; function to order the data by the number of peptides and remove the entries for proteins lacking more than one peptide. Then sort by ratio and remove proteins that lack SILAC ratios (unquantified proteins).</li>
	<li>Convert SILAC ratios to a log2 values using the formula: &#8216;=log(SILAC Ratio,2)&#8217;, where &#8216;SILAC Ratio&#8217; is substituted for the cell identifier. 
	Note: As a SILAC ratio results in any data for proteins showing a decrease in abundance being restricted to values between 0 and 1, it is usual to convert a SILAC ratio to a log2 SILAC ratio, as this means proteins both increased or decreased in a sample are represented on a log scale where 2 or -2 represents a 4-fold increase or decrease in abundance, and 3 or -3 a 9-fold increase or decrease in abundance respectively. Following this transformation, the data should fit a Gaussian distribution centered around a log2 SILAC ratio of 0. An example of this is given in Figure 3.</li>
	<li>Create new columns in the excel file and calculate log2 SILAC ratios for all the sample/mock columns. For conversion of a mock/sample ratio to a sample/mock ratio, use the formula: &#8220;=1/ratio&#8221;</li>
</ol>
7. Data Analysis II: Selecting High Confidence Interactions for Further Study
<ol>
	<li>Open Graphpad Prism, select New&#62;Data Table and Graph ... Select &#8216;column&#8217; from the list on the left hand side of the window, and select the Enter/Import data&#62;Enter replica values, stacked into columns options. Press &#8216;Create&#8217;.</li>
	<li>Select and copy a given log2 Sample/Mock SILAC ratio column from the Excel spreadsheet into the new Prism file.</li>
	<li>Click the dropdown &#8216;Insert&#8217; menu and select &#8216;New analysis&#8217;. Under column analysis select &#8216;Frequency distribution&#8217;. Keeping the default options click &#8216;OK&#8217;. 
	Note: This step generates a histogram illustrating the number of proteins identified at a given ratio. This should form a Gaussian distribution.</li>
	<li>In the &#8216;Results&#8217; folder a new &#8216;Histogram&#8217; section will have been generated. Select the frequency distribution section.</li>
	<li>Click the dropdown &#8216;Insert&#8217; menu and select New analysis&#62;XY&#62;Nonlinear regression (curve fit). Click on &#8216;Gaussian distribution&#8217; and click OK. 
	Note: This step fits a curve to the frequency distribution data generated in step 7.3, yielding values that can subsequently be used to calculate thresholds for significance.</li>
	<li>In the results window that appears, the mean and standard deviation are given. Generate a threshold by adding 1.96 standard deviations to the mean. 
	Note: These values represent the mean and standard deviations of the Gaussian distribution, not the total dataset. 1.96 standard deviations would place a threshold at the 95% confidence limit (p&#8804;0.05). 2.58 SD would give 99%, and 3.3 SD 99.9%. The threshold should be determined individually for each replica experiment as it may vary.</li>
</ol>
8. Data Analysis III: Merging Replica Datasets
Note: To be confident in identifying interaction partners a typical SILAC pulldown experiment should ideally be performed three times, with the medium and heavy labeled samples switched for one of the repeats to control for any effect of the media on the results. A protein that shows up as interacting in at least two of the three experiments, with the &#8216;switched&#8217;-media sample ideally representing one of these, is a high confidence interaction.
<ol>
	<li>Return to the Excel file, create a new tab called &#8216;Combined&#8217;. Label column A &#8216;Accession&#8217; and copy all the accession numbers from each of the individual experiments into this single column.</li>
	<li>Select the &#8216;Data&#8217; tab, and then the &#8216;Remove Duplicates&#8217; option.</li>
	<li>Create columns for the SILAC ratios, and ratio variability from each experiment as well as for the protein name/description column.</li>
	<li>In the description tab, use the vlookup formula to collect the description of each accession number. Drag the formula down the column to fill in the protein description. The vlookup formula is: &#8220;=vlookup(AccessionNo, WheretoLook, ColumnsAccross, False)&#8221;, where: AccessionNo is $aX with X being the row number. WheretoLook is the &#8216;Experiment tab Name&#8217;!$a$2:$Y$Z where Y and Z are the bottom right piece of data on the spreadsheet being referenced. ColumnsAccross is the number of columns across from the accession number in column A a desired value lies. 
	Note: For example, if the Accession number is in column A, and the data of interest lies in column E. The value here would be 5 (column A counts as 1). As accession numbers were pooled in step b, it is most likely that vlookup will not obtain all the required names from one experiment. Where it returns N/A, modify the formula to obtain the description from each experiment in turn until all the descriptions are acquired.</li>
	<li>To highlight interacting proteins in the combined dataset, select each ratio column individually and click on the Home tab, followed by Conditional Formatting&#62;Highlight cells rules&#62;More rules.</li>
	<li>Select style &#8216;Classic&#8217;. Format only cells that contain &#8216;Cell value&#8217;, &#8216;Greater than or Equal to&#8217;. In the box, type in the 1.96 standard deviation value, and click &#8216;OK&#8217;.</li>
	<li>Assess the ratio variability for each positive interaction. If subtracting the % variability would drop a &#8216;hit&#8217; below the threshold value, this should be treated with caution. 
	Note: This represents how consistent the individual ratios for each peptide that together make up a proteins&#8217; SILAC ratio are, and is given as a percentage. When the ratio variability is taken into account and a protein gives a SILAC ratio above the threshold, this protein represents a hit. If the ratio variability gives a range that falls below the threshold, the &#8216;hit&#8217; may represent a contaminant.</li>
	<li>Repeat this for each of the results columns and compare across experiments. Highlighted proteins identified in two or more experiments represent a high confidence interaction. 
	Note: Depending on the database used to assign protein accession numbers, in different experiments a protein may have been identified under a different, or even multiple accession numbers. It is important to check this to ensure that proteins are not accidentally omitted.</li>
</ol>

<ol><li>Williamson, M. P., Sutcliffe, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein-protein+interactions%5BTitle%5D)+AND+%22Biochem+Soc+Trans%22%5BJournal%5D)">Protein-protein interactions.</a> Biochem Soc Trans. 38, 875-878 (2010).</li>
<li>Bailey, D., Urena, L., Thorne, L., Goodfellow, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+of+protein+interacting+partners+using+tandem+affinity+purification%5BTitle%5D)+AND+%22J.+Vis.+Exp%22%5BJournal%5D)">Identification of protein interacting partners using tandem affinity purification.</a> J. Vis. Exp. (60), (2012).</li>
<li>Trinkle-Mulcahy, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Resolving+protein+interactions+and+complexes+by+affinity+purification+followed+by+label-based+quantitative+mass+spectrometry%5BTitle%5D)+AND+%22Proteomics%22%5BJournal%5D)">Resolving protein interactions and complexes by affinity purification followed by label-based quantitative mass spectrometry.</a> Proteomics. 12, 1623-1638 (2012).</li>
<li>Emmott, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+cellular+interactome+of+the+coronavirus+infectious+bronchitis+virus+nucleocapsid+protein+and+functional+implications+for+virus+biology%5BTitle%5D)+AND+%22J+Virol%22%5BJournal%5D)">The cellular interactome of the coronavirus infectious bronchitis virus nucleocapsid protein and functional implications for virus biology.</a> J Virol. 87, 9486-9500 (2013).</li>
<li>Li, W., Belsham, G. J., Proud, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Eukaryotic+initiation+factors+4A+%28eIF4A%29+and+4G+%28eIF4G%29+mutually+interact+in+a+1%3A1+ratio+in+vivo%5BTitle%5D)+AND+%22J+Biol+Chem%22%5BJournal%5D)">Eukaryotic initiation factors 4A (eIF4A) and 4G (eIF4G) mutually interact in a 1:1 ratio in vivo.</a> J Biol Chem. 276, 29111-29115 (2001).</li>
<li>Nielsen, P. J., Trachsel, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+mouse+protein+synthesis+initiation+factor+4A+gene+family+includes+two+related+functional+genes+which+are+differentially+expressed%5BTitle%5D)+AND+%22EMBO+J%22%5BJournal%5D)">The mouse protein synthesis initiation factor 4A gene family includes two related functional genes which are differentially expressed.</a> EMBO J. 7, 2097-2105 (1988).</li>
<li>Galicia-Vazquez, G., Cencic, R., Robert, F., Agenor, A. Q., Pelletier, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+cellular+response+linking+eIF4AI+activity+to+eIF4AII+transcription%5BTitle%5D)+AND+%22RNA%22%5BJournal%5D)">A cellular response linking eIF4AI activity to eIF4AII transcription.</a> RNA. 18, 1373-1384 (2012).</li>
<li>Zakowicz, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mutational+analysis+of+the+DEAD-box+RNA+helicase+eIF4AII+characterizes+its+interaction+with+transformation+suppressor+Pdcd4+and+eIF4GI%5BTitle%5D)+AND+%22RNA%22%5BJournal%5D)">Mutational analysis of the DEAD-box RNA helicase eIF4AII characterizes its interaction with transformation suppressor Pdcd4 and eIF4GI.</a> RNA. 11, 261-274 (2005).</li>
<li>Rothbauer, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+versatile+nanotrap+for+biochemical+and+functional+studies+with+fluorescent+fusion+proteins%5BTitle%5D)+AND+%22Mol+Cell+Proteomics%22%5BJournal%5D)">A versatile nanotrap for biochemical and functional studies with fluorescent fusion proteins.</a> Mol Cell Proteomics. 7, 282-289 (2008).</li>
<li>Trinkle-Mulcahy, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identifying+specific+protein+interaction+partners+using+quantitative+mass+spectrometry+and+bead+proteomes%5BTitle%5D)+AND+%22J+Cell+Biol%22%5BJournal%5D)">Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes.</a> J Cell Biol. 183, 223-239 (2008).</li>
<li>Dobreva, I., Fielding, A., Foster, L. J., Dedhar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mapping+the+integrin-linked+kinase+interactome+using+SILAC%5BTitle%5D)+AND+%22J+Proteome+Res%22%5BJournal%5D)">Mapping the integrin-linked kinase interactome using SILAC.</a> J Proteome Res. 7, 1740-1749 (2008).</li>
<li>Mittler, G., Butter, F., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+SILAC-based+DNA+protein+interaction+screen+that+identifies+candidate+binding+proteins+to+functional+DNA+elements%5BTitle%5D)+AND+%22Genome+research%22%5BJournal%5D)">A SILAC-based DNA protein interaction screen that identifies candidate binding proteins to functional DNA elements.</a> Genome research. 19, 284-293 (2009).</li>
<li>Selbach, M., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+interaction+screening+by+quantitative+immunoprecipitation+combined+with+knockdown+QUICK%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Protein interaction screening by quantitative immunoprecipitation combined with knockdown QUICK.</a> Nat Methods. 3, 981-983 (2006).</li>
<li>Boulon, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Establishment+of+a+protein+frequency+library+and+its+application+in+the+reliable+identification+of+specific+protein+interaction+partners%5BTitle%5D)+AND+%22Mol+Cell+Proteomics%22%5BJournal%5D)">Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners.</a> Mol Cell Proteomics. 9, 861-879 (2010).</li>
<li>Wang, X., Huang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identifying+dynamic+interactors+of+protein+complexes+by+quantitative+mass+spectrometry%5BTitle%5D)+AND+%22Mol+Cell+Proteomics%22%5BJournal%5D)">Identifying dynamic interactors of protein complexes by quantitative mass spectrometry.</a> Mol Cell Proteomics. 7, 46-57 (2008).</li>
<li>Fang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+the+human+COP9+signalosome+complex+using+affinity+purification+and+mass+spectrometry%5BTitle%5D)+AND+%22J+Proteome+Res%22%5BJournal%5D)">Characterization of the human COP9 signalosome complex using affinity purification and mass spectrometry.</a> J Proteome Res. 7, 4914-4925 (2008).</li>
</ol>

The authors declare that they have no competing financial interests.

The SILAC pulldown strategy described here represents a very sensitive and powerful means of detecting novel protein:protein interactions, and furthermore allows the rapid and simple discrimination of altered binding patterns between closely related samples of interest. In this example this technique was used to investigate the protein:protein interactions of the eIF4AI and eIF4AII proteins6. To the author&#8217;s knowledge, this is the first study in the literature exploiting the utility of SILAC proteomics to investigate the cellular interactome of these two isoforms of eIF4A.
The approach as described above uses a GFP-tag and anti-GFP beads9,10 and therefore modifications may be required to enable this approach to be used for a specific protein of interest, for example whether the tag is placed at the N- or C-terminus of a protein. Where possible, western blots or functional assays should be performed to detect binding of a known protein interaction partner. Should a protein not tolerate fusion with a GFP tag, other tagging or pulldown strategies have been applied to SILAC pulldowns using both other tags (FLAG11, Biotin12, STREP (own data, unpublished)), or by using primary antibodies against a protein of interest where siRNA knockdown of the target protein provides a control sample13. Such experiments have been described elsewhere in the literature, but in brief, step 1 and steps 5.4-8 would be applied as above, with steps 2-5.3 modified as appropriate for the expression/pulldown system of choice using equal protein inputs as described in steps 2.4-2.5. As the quantification stages allow non-specific binding proteins to be removed at the analysis level, it is recommended to omit pre-incubation with control beads, or high salt washes in order to preserve low-affinity protein:protein interactions with a protein of interest. A nuclease may be included or omitted from this protocol according to the specifics of a particular experiment. For example: as the proteins used in this method are RNA helicases, RNase cocktail was included in the protocol to remove indirect interactions mediated via the RNA (step 3.2). In some cases however, there could be benefit from running parallel experiments with and without nuclease to identify nucleic acid dependent interactions.
Within this protocol, the switching of &#8216;medium&#8217; and &#8216;heavy&#8217; samples in replicate experiments is recommended to control for variation introduced by differences in the SILAC media or cell growth. An alternative control involves the sequential switching of all three (&#8216;light&#8217;, &#8216;medium&#8217;, and &#8216;heavy&#8217;) media in replica experiments. Whilst this approach is potentially more stringent, it does increase the complexity of the analysis, as in at least one replicate, a protein of interest will be produced in &#8216;light&#8217; labeled cells and so it is necessary to distinguish between proteins consistently identified in &#8216;light&#8217; samples (environmental contaminants such as keratins), and those that are only enriched in &#8216;light&#8217; samples when bound to a protein of interest.
Whilst the use of quantification data allows discrimination of specific- from non-specific interactions through use of a threshold, inevitably some genuine interactions may be discarded. The approach above is a simple and rapid approach to identifying protein:protein interactions that can be easily attempted by researchers with no previous experience with mass spectrometry, or analysis of large protein:protein interaction datasets. For most uses this is more than sufficient for identifying novel proteins of interest. Further modifications to this approach to help reduce this data loss are described elsewhere in the literature and include the use of a protein frequency library where known contaminant proteins for a specific set of experimental parameters (cell line, bead matrix, buffer conditions) may be excluded10,14. However, depending on the particular experimental parameters it may be necessary to run a number of control experiments to generate a bead proteome and this can therefore increase both the expense and complexity of the experiment. Further information on this technique is available from the www.peptracker.co.uk website14.
It should also be noted that the protocol described above involves mixing differently labeled samples at the end of the immunoprecipitation process (termed a Mixing After Purification &#8211; MAP SILAC experiment). This is done as protein:protein interactions occur at a given equilibrium15. It should be noted that other groups have combined this MAP SILAC approach described in this protocol with incubation of the samples prior to pulldown (Purification After Mixing &#8211; PAM SILAC) for different lengths of time (20 min to 2 hr have been used in the literature)15,16. Based on how rapidly a protein ratio drops towards 1:1, it is possible to qualitatively investigate binding affinities and to define proteins as stable or dynamic interacting proteins15.
In summary, SILAC pulldowns represent a very powerful means of identifying proteins interacting with a given protein of interest, in a physiologically relevant setting. The technique can be very easily adapted to a number of different purification strategies, allowing its application to any given protein of interest. Quantification of results vastly simplifies identification of genuine interactions, and permits relaxation of stringent buffer conditions used to remove non-specific binders, and thus preserves low affinity interactions. As up to three samples can be compared in the above strategy, the technique has clear strengths in comparing differences in protein binding between different protein isoforms, mutant proteins, or the effect of pharmacological inhibitors. As whole gel slices are analyzed rather than individual bands that stain by Coomassie, the numbers of proteins identified at high confidence are typically higher than those identified in a standard GST/TAP-pulldown, and experimenter bias in selecting proteins of interest is removed. The technique therefore compares very favorably with other commonly used techniques used in identification of novel protein-interactions (yeast 2-hybrid, GST/His or TAP Pulldowns).

An essential step in understanding protein function is identification of relevant interacting proteins. Where such proteins are unknown there are a number of techniques available, each with their own merits and drawbacks. These include the yeast two-hybrid system, pulldown assays using recombinant protein, as well as tandem affinity purification or TAP-tagging1,2.
A more recent addition to these techniques is the combination of affinity purification of a protein of interest from a relevant mammalian cell line, followed by quantitative mass spectrometry using stable isotope labeling of amino acids in cell culture (SILAC)3. This has advantages over the yeast two-hybrid approach in that cell localization and post-translational modifications are not perturbed, as well as advantages over traditional TAP-tagging in that it is a quantitative rather than qualitative approach allowing the user to readily distinguish non-specifically interacting proteins and contaminants, from host factors that bind specifically. Further, as a sample is typically analyzed whole, rather than as individual protein bands, proteins of interest are not masked by similarly migrating proteins on a gel, nor do they typically need to be present at sufficient levels to be visible after staining, leading to increased numbers of confidently identified proteins 4.
To demonstrate this technique, GFP fusions of the closely related eukaryotic translation initiation factor eIF4AI and eIF4AII that share over 90% amino acid identity were investigated by SILAC-immunoprecipitation quantitative proteomics. Human eIF4AI and II were cloned into pEGFP-C1 to form a fusion protein where GFP is fused to the N-terminus of eIF4A. To avoid the need for generating stable cell lines transient transfection was used to deliver these constructs to stable isotope labeled 293T cells.
Cells were first labeled for two weeks in SILAC cell culture media, followed by transfection of plasmid DNA encoding a protein of interest. Cells were then lysed, the protein concentration normalized, and equal amounts of lysate affinity purified on anti-GFP agarose. Equal amounts of eluate were then combined and submitted for LC-MS/MS analysis. The results of this analysis are then processed to identify high confidence protein:protein interactions (Figure 1).
SILAC immunoprecipitation enables the identification of not only direct interactions but also low affinity or indirect interactions with protein complexes4. Using this system, eIF4AI and II immunoprecipitations allowed reproducible and confident identification of the primary binding partner eIF4G (isoforms I/II and III)5, as well as indirect interactions with eIF4E, and numerous components of the eIF3 complex.

<table><tbody><tr><td>Dulbecco&#039;s Modified Eagle Medium &amp;nbsp;(DMEM)</td><td>Dundee Cell Products</td><td>LM010</td><td>DMEM lacking Arginine and Lysine. (and containing L-glutamine)</td></tr><tr><td>Dialysed FBS (10 kDa cutoff) 500 ml</td><td>Dundee Cell Products</td><td>DS1003</td><td></td></tr><tr><td>Arginine (R0) 25 g</td><td>Sigma-Aldrich</td><td>A8094</td><td>Resuspend 0.5 g in 6 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 84 mg/ml stock)</td></tr><tr><td>Arginine (R6) 0.5 g</td><td>Cambridge Isotope Labs</td><td>CLM-2265</td><td>Resuspend 0.5 g in 6 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 84 mg/ml stock)</td></tr><tr><td>Arginine (R10) 0.5 g</td><td>Cambridge Isotope Labs</td><td>CNLM-539</td><td>Resuspend 0.5 g in 6 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 84 mg/ml stock)</td></tr><tr><td>Lysine (K0) 25 g</td><td>Sigma-Aldrich</td><td>L8662</td><td>Resuspend 0.5 g in 3.5 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 146 mg/ml stock)</td></tr><tr><td>Lysine (K4) 0.5 g</td><td>Cambridge Isotope Labs</td><td>DLM-2640</td><td>Resuspend 0.5 g in 3.5 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 146 mg/ml stock)</td></tr><tr><td>Lysine (K8) 0.5 g</td><td>Cambridge Isotope Labs</td><td>CNLM-291</td><td>Resuspend 0.5 g in 3.5 ml PBS, filter sterilize, and freeze in 0.5 ml aliquots (gives a 146 mg/ml stock)</td></tr><tr><td>Penicillin/streptomycin, Liquid. 100 ml</td><td>Gibco</td><td>15140-122</td><td></td></tr><tr><td>Lipofectamine 2000 transfection reagent</td><td>Invitrogen</td><td>11668-027</td><td></td></tr><tr><td>GFP-trap Agarose (500 &amp;mu;l resin)</td><td>Chromotek</td><td>gta-20</td><td></td></tr><tr><td>5x SDS Sample loading buffer</td><td>Fisher Scientific</td><td>PN39000</td><td>The use of purchased rather than homemade sample buffer is recommended to minimize keratin contamination.</td></tr><tr><td>Protease inhibitor cocktail set III</td><td>Calbiochem</td><td>539134</td><td></td></tr><tr><td>1.7 ml prelubricated tubes</td><td>Costar</td><td>3207</td><td></td></tr><tr><td>BCA protein assay kit (1 L)</td><td>Pierce</td><td>23225</td><td></td></tr><tr><td>Tris (Trizma)</td><td>Sigma-Aldrich</td><td>T1503</td><td></td></tr><tr><td>Ethylenediaminetetraacetic acid (EDTA)</td><td>Sigma-Aldrich</td><td>E6758</td><td></td></tr><tr><td>Sodium chloride</td><td>Sigma-Aldrich</td><td>S3014</td><td></td></tr><tr><td>Rnase cocktail</td><td>Ambion</td><td>AM2286</td><td></td></tr><tr><td>NP-40 alternative</td><td>Millipore</td><td>492016</td><td></td></tr></tbody></table>

identification of protein interaction partners in mammalian cells using silac-immunoprecipitation quantitative proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.

In a typical SILAC pulldown experiment, the vast majority of identified proteins (&#62;90%) represent contaminants as well as proteins binding non-specifically to the affinity matrix and this is illustrated in Figure 2B, even when washing protocols remove a majority of cytoplasmic contaminants such as GAPDH (Figure 2A). However, the clustering of non-specifically binding proteins in a normal distribution allows proteins that specifically bind to a protein of interest to be distinguished as these have higher sample/mock ratios than the background. Whilst contaminants should theoretically cluster around a log2 SILAC ratio of 0, this is not necessarily the case, an example of this is given in Figure 3. Possible reasons for this include imperfect SILAC labeling of cells, loading unequal volumes or concentrations of lysate onto the anti-GFP beads, accidental loss of beads during purification or unequal mixing of samples at the end of the purification procedure3. However, assuming data are analyzed based on a threshold standard deviation from the mean of the normally distributed contaminants, minor shifts in the centering of the data should not affect the quality of the results.
When comparing differences in protein interactions between two related proteins, a similar situation may occur where one protein of interest is produced to higher levels within cells than a second (either due to variation in transfection efficiency, or an intrinsic property of the protein or mRNA). Some variation in expression (e.g.,&#160;Figure 2A) can be corrected for by analyzing the SILAC ratio for these two samples. In this example these would be the GFP-eIF4AI and GFP-eIF4AII samples. By analyzing the 4AI/4AII SILAC ratio as discussed in section 7, it is possible to identify proteins whose binding varies significantly between isoforms.
In Figure 4, a representation of the eIF4A-binding proteins identified in one of the replica experiments conducted is shown illustrating the coverage of the initiation factor complex this protocol achieved. The highest ratios were typically observed with eIF4G, which binds directly to eIF4A, with lower ratios for eIF4E, which binds to eIF4G at a site away from the eIF4A-binding site. Lower ratios were observed for members of the eIF3 complex. However, this clearly illustrates that the experiment satisfactorily identified both direct and indirect binding partners of eIF4AI and II. As might be expected from their high sequence identity6, protein:protein interactions appeared largely conserved between the two isoforms in this experimental system7,8. A selection of some of the interacting proteins are given in Table 2, illustrating the data format.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Column Heading</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>Accession</td>
			<td>Displays the accession number for the sequence</td>
		</tr>
		<tr>
			<td>Coverage</td>
			<td>Proportion of the protein sequence covered by the identified peptides</td>
		</tr>
		<tr>
			<td>&#9839;PSM</td>
			<td>Peptide spectral match</td>
		</tr>
		<tr>
			<td>&#9839;peptides</td>
			<td>Total number of unique peptides identified for a protein</td>
		</tr>
		<tr>
			<td>&#9839;AAs</td>
			<td>The length of a protein in amino acids</td>
		</tr>
		<tr>
			<td>MW (Da)</td>
			<td>The molecular weight of a protein in Daltons. Excludes modifications</td>
		</tr>
		<tr>
			<td>calc. PI</td>
			<td>The theoretical isoelectric point of a protein</td>
		</tr>
		<tr>
			<td>Score</td>
			<td>The total score of a protein (which represents the sum of the individual peptide scores). The exact score required for significance will vary between experiments. A MS facility will usually apply a 5% false discovery rate cutoff.</td>
		</tr>
		<tr>
			<td>Sequence</td>
			<td>The sequence of amino acids constituting the protein</td>
		</tr>
		<tr>
			<td>Ratio</td>
			<td>The relative intensity of peptides in a named labeled sample, compared to a second labeled sample</td>
		</tr>
		<tr>
			<td>Ratio Count</td>
			<td>The number of peptide ratios that were used to calculate the a given protein ratio</td>
		</tr>
		<tr>
			<td>Ratio variability (%)</td>
			<td>The variability of the individual peptide ratios used to calculate a given protein ratio</td>
		</tr>
		<tr>
			<td>Description</td>
			<td>The name of the protein</td>
		</tr>
	</tbody>
</table>
Table 1. Standard column headings from a Proteome Discoverer report. Whilst useful information can be gained from all of these columns, those critical for this analysis are shown in bold.
<table border="0" cellpadding="0" cellspacing="0" width="1057">
	<tbody>
		<tr>
			<td>Accession</td>
			<td>Peptides</td>
			<td>4AI/Mock</td>
			<td>4AII/Mock</td>
			<td>Name</td>
			<td>SILAC analysis</td>
		</tr>
		<tr>
			<td>A8K7F6</td>
			<td>21</td>
			<td>100</td>
			<td>1</td>
			<td>eIF4AI</td>
			<td rowspan="2">&#8216;Bait&#8217; protein&#160;</td>
		</tr>
		<tr>
			<td>Q14240</td>
			<td>22</td>
			<td>0.01</td>
			<td>90.855</td>
			<td>eIF4AII</td>
		</tr>
		<tr>
			<td>G5E9S1</td>
			<td>25</td>
			<td>47.575</td>
			<td>30.53</td>
			<td>eIF4GI</td>
			<td rowspan="3">Interacting proteins</td>
		</tr>
		<tr>
			<td>Q59GJ0</td>
			<td>5</td>
			<td>11.778</td>
			<td>10.619</td>
			<td>eIF4GII&#160;</td>
		</tr>
		<tr>
			<td>P06730</td>
			<td>3</td>
			<td>7.22</td>
			<td>7.57</td>
			<td>eIF4E</td>
		</tr>
		<tr>
			<td>Q5T6W5</td>
			<td>4</td>
			<td>0.685</td>
			<td>0.646</td>
			<td>hnRNPK</td>
			<td rowspan="2">Non-specifically binding contaminants</td>
		</tr>
		<tr>
			<td>P62805</td>
			<td>1</td>
			<td>0.531</td>
			<td>0.498</td>
			<td>Histone H4</td>
		</tr>
		<tr>
			<td>H6VRG2</td>
			<td>18</td>
			<td>-</td>
			<td>-</td>
			<td>Keratin-1</td>
			<td rowspan="2">Environmental contaminants</td>
		</tr>
		<tr>
			<td>P35527</td>
			<td>11</td>
			<td>0.01</td>
			<td>0.01</td>
			<td>Keratin-1 cytoskeletal 9</td>
		</tr>
	</tbody>
</table>
Table 2. Typical Data from a SILAC immunoprecipitation experiment.&#160;Giving example data for a protein of interest/bait (high peptides, high ratio), proteins interacting with a protein of interest (high/low peptides, high ratio), non-specifically binding proteins (high/low peptides, ratio falls below cutoff &#8211; in this experiment 0.96), and environmental contaminants (often high peptides, negative ratio/below threshold).
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig1highres.jpg" src="/files/ftp_upload/51656/51656fig1.jpg" /> 
Figure 1. Experimental Plan. Firstly cells are grown in media lacking Arginine and Lysine and substituted with stable isotope labeled Arginine and Lysine for 2 weeks (1). (2) Cells are seeded into 10 cm2 dishes and transiently transfected with plasmids encoding GFP (Mock) or GFP fusion proteins (Samples). (3) Cells are lysed and GFP or GFP fusion proteins are immunoprecipitated from cell lysates. (4) Samples are combined in a 1:1 ratio and submitted for LC-MS/MS analysis. Data is then analyzed to remove low confidence protein identifications and to select a level of protein enrichment corresponding to genuine interacting proteins.
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig2highres.jpg" src="/files/ftp_upload/51656/51656fig2.jpg" /> 
Figure 2. Confirming suitable immunoprecipitation conditions. A) Western blot analysis of cell lysates, as well as the unbound and bound fractions from the immunoprecipitation confirms expression and immunoprecipitation of the protein of interest. A western blot against GAPDH confirms depletion of non-interacting proteins and a further western blot a known interacting partner of eIF4A confirms the successful immunoprecipitation of proteins binding to the protein of interest. B) Where interacting partners of a protein of interest are not known, a silver-stained gel may confirm immunoprecipitation of interacting proteins. On this silver-stained gel bands for GFP and GFP-eIF4AI/II are clear, and a band migrating at the correct size for eIF4G is present only in the GFP-4AI/II-bound lanes and not in the GFP control lane.
<img alt="Figure 3" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig3highres.jpg" src="/files/ftp_upload/51656/51656fig3.jpg" /> 
Figure 3. Representative results. Histogram showing the distribution of protein ratios from one repeat of (A) GFP-eIF4AI or (B) GFP-eIF4AII pulldown. The 1.96 standard deviation cutoff is marked with a dashed line. Interacting proteins falling outside the normally-distributed contaminants are evident from ~0.25 and 1 in (A), and ~0.3-1.5 in (B).
<img alt="Figure 4" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig4highres.jpg" src="/files/ftp_upload/51656/51656fig4.jpg" /> 
Figure 4. Identification of eIF complex members from a single replica of a SILAC IP experiment. Proteins in green were the protein of interest used for the pulldown Interacting proteins are shaded from red to white according to log2 SILAC ratio in the SILAC IP with red being the most abundant protein in the analysis, and white being the 1.96 SD cutoff. Proteins shaded in grey were not identified in this analysis.

Watch this Scientific Journal Video about Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics at JoVE.com

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

SILAC immunoprecipitation experiments represent a powerful means for discovering novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants, and low affinity interactions preserved through use of less-stringent buffer conditions.

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel ...

identification-protein-interaction-partners-mammalian-cells-using

Universidad San Sebastian

Research

JoVE Journal

Biology

31.4K Views.  University of Cambridge.  SILAC immunoprecipitatie experimenten vormen een krachtig middel voor het ontdekken van nieuwe eiwit: eiwit interacties. Doordat de nauwkeurige relatieve kwantificering van eiwitgehalte in zowel controle monsters, waar interacties gemakkelijk kan worden onderscheiden van experimenteel verontreinigingen en lage affiniteit interacties bewaard door het gebruik van minder stringente buffer omstandigheden. 

Video: Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Protein Complex Affinity Capture from Cryomilled Mammalian Cells

Affinity capture is an effective technique for isolating endogenous protein complexes for further study. When used in conjunction with an antibody, this technique is also frequently referred to as immunoprecipitation. Affinity capture can be applied in a bench-scale and in a high-throughput context. When coupled with protein mass spectrometry, affinity capture has proven to be a workhorse of interactome analysis. Although there are potentially many ways to execute the numerous steps involved, the following protocols implement our favored methods. Two features are distinctive: the use of cryomilled cell powder to produce cell extracts, and antibody-coupled paramagnetic beads as the affinity medium. In many cases, we have obtained superior results to those obtained with more conventional affinity capture practices. Cryomilling avoids numerous problems associated with other forms of cell breakage. It provides efficient breakage of the material, while avoiding denaturation issues associated with heating or foaming. It retains the native protein concentration up to the point of extraction, mitigating macromolecular dissociation. It reduces the time extracted proteins spend in solution, limiting deleterious enzymatic activities, and it may reduce the non-specific adsorption of proteins by the affinity medium. Micron-scale magnetic affinity media have become more commonplace over the last several years, increasingly replacing the traditional agarose- and Sepharose-based media. Primary benefits of magnetic media include typically lower non-specific protein adsorption; no size exclusion limit because protein complex binding occurs on the bead surface rather than within pores; and ease of manipulation and handling using magnets.

Here we describe protocols to disrupt mammalian cells by solid-state milling at a cryogenic temperature, produce a cell extract from the resulting cell powder, and isolate protein complexes of interest by affinity capture upon antibody-coupled micron-scale paramagnetic beads.

Affinity capture is an effective technique for isolating endogenous protein complexes for further study. When used in conjunction with an antibody, this ...

Biochemistry

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

We have established a method for the purification of coregulatory interaction proteins using the LC-MS/MS system.

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in ...

Analysis of Protein-protein Interactions and Co-localization Between Components of Gap, Tight, and Adherens Junctions in Murine Mammary Glands

Cell-cell interactions play a pivotal role in preserving tissue integrity and the barrier between the different compartments of the mammary gland. These interactions are provided by junctional proteins that form nexuses between adjacent cells. Junctional protein mislocalization and reduced physical associations with their binding partners can result in the loss of function and, consequently, to organ dysfunction. Thus, identifying protein localization and protein-protein interactions (PPIs) in normal and disease-related tissues is essential to finding new evidences and mechanisms leading to the progression of diseases or alterations in developmental status. This manuscript presents a two-step method to evaluate PPIs in murine mammary glands. In protocol section 1, a method to perform co-immunofluorescence (co-IF) using antibodies raised against the proteins of interest, followed by secondary antibodies labeled with fluorochromes, is described. Although co-IF allows for the demonstration of the proximity of the proteins, it does make it possible to study their physical interactions. Therefore, a detailed protocol for co-immunoprecipitation (co-IP) is provided in protocol section 2. This method is used to determine the physical interactions between proteins, without confirming whether these interactions are direct or indirect. In the last few years, co-IF and co-IP techniques have demonstrated that certain components of intercellular junctions co-localize and interact together, creating stage-dependent junctional nexuses that vary during mammary gland development.

Intercellular junctions are requisites for mammary gland stage-specific functions and development. This manuscript provides a detailed protocol for the study of protein-protein interactions (PPIs) and co-localization using murine mammary glands. These techniques allow for the investigation of the dynamics of the physical association between intercellular junctions at different developmental stages.

Cell-cell interactions play a pivotal role in preserving tissue integrity and the barrier between the different compartments of the mammary gland. These ...

Developmental Biology

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Identification of protein complexes with quantitative proteomics in S. cerevisiae

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as important intracellular signalling molecules. However, unlike proteins, lipids are small hydrophobic molecules that traffic primarily by poorly described nonvesicular routes, which are hypothesized to occur at membrane contact sites (MCSs). MCSs are regions where the endoplasmic reticulum (ER) makes direct physical contact with a partnering organelle, e.g., plasma membrane (PM). The ER portion of ER-PM MCSs is enriched in lipid-synthesizing enzymes, suggesting that lipid synthesis is directed to these sites and implying that MCSs are important for lipid traffic. Yeast is an ideal model to study ER-PM MCSs because of their abundance, with over 1000 contacts per cell, and their conserved nature in all eukaryotes. Uncovering the proteins that constitute MCSs is critical to understanding how lipids traffic is accomplished in cells, and how they act as signaling molecules. We have found that an ER called Scs2p localize to ER-PM MCSs and is important for their formation. We are focused on uncovering the molecular partners of Scs2p. Identification of protein complexes traditionally relies on first resolving purified protein samples by gel electrophoresis, followed by in-gel digestion of protein bands and analysis of peptides by mass spectrometry. This often limits the study to a small subset of proteins. Also, protein complexes are exposed to denaturing or non-physiological conditions during the procedure. To circumvent these problems, we have implemented a large-scale quantitative proteomics technique to extract unbiased and quantified data. We use stable isotope labeling with amino acids in cell culture (SILAC) to incorporate staple isotope nuclei in proteins in an untagged control strain. Equal volumes of tagged culture and untagged, SILAC-labeled culture are mixed together and lysed by grinding in liquid nitrogen. We then carry out an affinity purification procedure to pull down protein complexes. Finally, we precipitate the protein sample, which is ready for analysis by high-performance liquid chromatography/ tandem mass spectrometry. Most importantly, proteins in the control strain are labeled by the heavy isotope and will produce a mass/ charge shift that can be quantified against the unlabeled proteins in the bait strain. Therefore, contaminants, or unspecific binding can be easily eliminated. By using this approach, we have identified several novel proteins that localize to ER-PM MCSs. Here we present a detailed description of our approach. 

Here we describe a new quantitative proteomics technique for identifying protein complexes in Saccharomyces cerevisiae. In this study, we have used the SILAC method together with affinity purification followed by tandem mass spectrometry to identify with high specificity the binding partners of an ER protein, Scs2p.

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

Proteomics is the large-scale analysis of proteins. Proteomic techniques, such as liquid chromatography tandem mass spectroscopy (LC-MS/MS), can characterize thousands of proteins at a time. These powerful techniques allow us to have a systemic understanding of cellular changes, especially when cells are subjected to various stimuli, such as infections, stresses, and specific test conditions. Even with recent developments, analyzing the exosomal proteome is time-consuming and often involves complex methodologies. In addition, the resultant large dataset often needs robust and streamlined analysis in order for researchers to perform further downstream studies. Here, we describe a SILAC-based protocol for characterizing the exosomal proteome when cells are infected with HIV-1. The method is based on simple isotope labeling, isolation of exosomes from differentially labeled cells, and mass spectrometry analysis. This is followed by detailed data mining and bioinformatics analysis of the proteomic hits. The resultant datasets and candidates are easy to understand and often offer a wealth of information that is useful for downstream analysis. This protocol is applicable to other subcellular compartments and a wide range of test conditions.

Here, we describe a quantitative proteomics method using the technique of stable isotope labeling by amino acids in cell culture (SILAC) to analyze the effects of HIV-1 infection on host exosomal proteomes. This protocol can be easily adapted to cells under different stress or infection conditions.

Proteomics is the large-scale analysis of proteins. Proteomic techniques, such as liquid chromatography tandem mass spectroscopy (LC-MS/MS), can ...

Immunology and Infection

Identification of Protein Interacting Partners Using Tandem Affinity Purification

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein partners. There are many approaches that allow the identification of interacting partners, including the yeast two hybrid system, as well as pull down assays using recombinant proteins and immunoprecipitation of endogenous proteins followed by mass spectrometry identification1. Recent studies have highlighted the utility of double-affinity tag mediated purification, coupled with two specific elution steps in the identification of interacting proteins. This approach, termed Tandem Affinity Purification (TAP), was initially used in yeast2,3 but more recently has been adapted to use in mammalian cells4-8.
As proof-of-concept we have established a tandem affinity purification (TAP) method using the well-characterized eukaryotic translation initiation factor eIF4E9,10.The cellular translation factor eIF4E is a critical component of the cellular eIF4F complex involved in cap-dependent translation initiation10. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence8. To forgo the need for the generation of clonal cell lines, we developed a rapid system that relies on the expression of the TAP-tagged bait protein from an episomally maintained plasmid based on pMEP4 (Invitrogen). Expression of tagged murine eIF4E from this plasmid was controlled using the cadmium chloride inducible metallothionein promoter.
Lysis of the expressing cells and subsequent affinity purification via binding to rabbit IgG agarose, TEV protease cleavage, binding to streptavidin linked agarose and subsequent biotin elution identified numerous proteins apparently specific to the eIF4E pull-down (when compared to control cell lines expressing the TAP tag alone). The identities of the proteins were obtained by excision of the bands from 1D SDS-PAGE and subsequent tandem mass spectrometry. The identified components included the known eIF4E binding proteins eIF4G and 4EBP-1. In addition, other components of the eIF4F complex, of which eIF4E is a component were identified, namely eIF4A and Poly-A binding protein. The ability to identify not only known direct binding partners as well as secondary interacting proteins, further highlights the utility of this approach in the characterization of proteins of unknown function.

Tandem affinity purification is a robust approach for the identification of protein binding partners. As proof of concept, this methodology was applied to the well-characterized translation initiation factor eIF4E to co-precipitate the host cell factors involved in translation initiation. This method is easily adapted to any cellular or viral protein.

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein ...

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.
Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

We present a variation of the QUICK (QUantitative Immunoprecipitation Combined with Knockdown) approach that was introduced previously to distinguish between true and false protein-protein interactions. Our approach is based on 15N metabolic labeling, the modulation of affinities of protein-protein interactions by the presence/absence of ATP, immunoprecipitation, and quantitative mass spectrometry.

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in ...

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

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