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>

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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.

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			<td height="42" style="height:42px;width:271px;">Dulbecco&#39;s Modified Eagle Medium &#160;(DMEM)</td>
			<td style="width:87px;">Dundee Cell Products</td>
			<td style="width:88px;">LM010</td>
			<td style="width:745px;">DMEM lacking Arginine and Lysine. (and containing L-glutamine)</td>
		</tr>
		<tr height="21">
			<td height="42" rowspan="2" style="height:42px;width:271px;">Dialysed FBS (10kDa cutoff) 500ml</td>
			<td rowspan="2" style="width:87px;">Dundee Cell Products</td>
			<td rowspan="2" style="width:88px;">DS1003</td>
			<td rowspan="2" style="width:745px;"></td>
		</tr>
		<tr height="21">
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Arginine (R0) 25g</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td style="width:88px;">A8094</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Arginine (R6) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CLM-2265</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Arginine (R10) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CNLM-539</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Lysine (K0) 25g</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td style="width:88px;">L8662</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Lysine (K4) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">DLM-2640</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Lysine (K8) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CNLM-291</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Penicillin-Streptomycin, Liquid. 100ml</td>
			<td style="width:87px;">Gibco</td>
			<td style="width:88px;">15140-122</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Lipofectamine 2000 transfection reagent</td>
			<td style="width:87px;">Invitrogen</td>
			<td style="width:88px;">11668-027</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">GFP-trap Agarose (500&#956;l resin)</td>
			<td style="width:87px;">Chromotek</td>
			<td style="width:88px;">gta-20</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">5x SDS Sample loading buffer</td>
			<td style="width:87px;">Fisher Scientific</td>
			<td style="width:88px;">PN39000</td>
			<td style="width:745px;">The use of purchased rather than homemade sample buffer is recommended to minimize keratin contamination.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Protease inhibitor cocktail set III</td>
			<td style="width:87px;">Calbiochem</td>
			<td style="width:88px;">539134</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">1.7ml&#160; prelubricated tubes</td>
			<td style="width:87px;">Costar</td>
			<td style="width:88px;">3207</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">BCA protein assay kit (1L)</td>
			<td style="width:87px;">Pierce</td>
			<td style="width:88px;">23225</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Tris (Trizma)</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>T1503</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Ethylenediaminetetraacetic acid (EDTA)</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>E6758</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Sodium Chloride</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>S3014</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Rnase cocktail</td>
			<td style="width:87px;">Ambion</td>
			<td>AM2286&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">NP-40 alternative</td>
			<td style="width:87px;">Millipore</td>
			<td align="right">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

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JoVE Journal

Biology

31.3K Views.  University of Cambridge. 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.

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

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 ...

Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay1 allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)2 and the visualization of their interactions mediated by BiFC in onion epidermal cells.

Bimolecular Fluorescence Complementation BiFC Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

This article illustrates how to properly use the BioRad Helios Gene Gun to introduce plasmid DNA into onion epidermal cells and how to test for protein-protein interactions in onion cells based on the principle of Bimolecular Fluorescence Complementation (BiFC)

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular ...

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 ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

siRNA Screening to Identify Ubiquitin and Ubiquitin-like System Regulators of Biological Pathways in Cultured Mammalian Cells

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that regulates diverse biological pathways including the hypoxia response, proteostasis, the DNA damage response and transcription.&#160; To better understand how UBLs regulate pathways relevant to human disease, we have compiled a human siRNA &#8220;ubiquitome&#8221; library consisting of 1,186 siRNA duplex pools targeting all known and predicted components of UBL system pathways. This library can be screened against a range of cell lines expressing reporters of diverse biological pathways to determine which UBL components act as positive or negative regulators of the pathway in question.&#160; Here, we describe a protocol utilizing this library to identify ubiquitome-regulators of the HIF1A-mediated cellular response to hypoxia using a transcription-based luciferase reporter.&#160; An initial assay development stage is performed to establish suitable screening parameters of the cell line before performing the screen in three stages: primary, secondary and tertiary/deconvolution screening. &#160;The use of targeted over whole genome siRNA libraries is becoming increasingly popular as it offers the advantage of reporting only on members of the pathway with which the investigators are most interested.&#160; Despite inherent limitations of siRNA screening, in particular false-positives caused by siRNA off-target effects, the identification of genuine novel regulators of the pathways in question outweigh these shortcomings, which can be overcome by performing a series of carefully undertaken control experiments.

Here, we describe a methodology to perform a targeted siRNA &#8220;ubiquitome&#8221; screen to identify novel ubiquitin and ubiquitin-like regulators of the HIF1A-mediated cellular response to hypoxia.&#160; This can be adapted to any biological pathway where a robust read out of reporter activity is available.

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that ...

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may result in disease, making protein interactions important drug targets. It is thus highly important to understand these interactions at the molecular level. Protein interactions are studied using a variety of techniques ranging from cellular and biochemical assays to quantitative biophysical assays, and these may be performed either with full-length proteins, with protein domains or with peptides. Peptides serve as excellent tools to study protein interactions since peptides can be easily synthesized and allow the focusing on specific interaction sites. Peptide arrays enable the identification of the interaction sites between two proteins as well as screening for peptides that bind the target protein for therapeutic purposes. They also allow high throughput SAR studies. For identification of binding sites, a typical peptide array usually contains partly overlapping 10-20 residues peptides derived from the full sequences of one or more partner proteins of the desired target protein. Screening the array for binding the target protein reveals the binding peptides, corresponding to the binding sites in the partner proteins, in an easy and fast method using only small amount of protein.
In this article we describe a protocol for screening peptide arrays for mapping the interaction sites between a target protein and its partners. The peptide array is designed based on the sequences of the partner proteins taking into account their secondary structures. The arrays used in this protocol were Celluspots arrays prepared by INTAVIS Bioanalytical Instruments. The array is blocked to prevent unspecific binding and then incubated with the studied protein. Detection using an antibody reveals the binding peptides corresponding to the specific interaction sites between the proteins.

Peptide array screening is a high throughput assay for identifying protein-protein interaction sites. This allows mapping multiple interactions of a target protein and can serve as a method for identifying sites for inhibitors that target a protein. Here we describe a protocol for screening and analyzing peptide arrays.

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...

Direct Protein Delivery to Mammalian Cells Using Cell-permeable Cys2-His2 Zinc-finger Domains

Due to their modularity and ability to be reprogrammed to recognize a wide range of DNA sequences, Cys2-His2 zinc-finger DNA-binding domains have emerged as useful tools for targeted genome engineering. Like many other DNA-binding proteins, zinc-fingers also possess the innate ability to cross cell membranes. We recently demonstrated that this intrinsic cell-permeability could be leveraged for intracellular protein delivery. Genetic fusion of zinc-finger motifs leads to efficient transport of protein and enzyme cargo into a broad range of mammalian cell types. Unlike other protein transduction technologies, delivery via zinc-finger domains does not inhibit enzyme activity and leads to high levels of cytosolic delivery. Here a detailed step-by-step protocol is presented for the implementation of zinc-finger technology for protein delivery into mammalian cells. Key steps for achieving high levels of intracellular zinc-finger-mediated delivery are highlighted and strategies for maximizing the performance of this system are discussed.

Direct Protein Delivery to Mammalian Cells Using Cell-permeable Cys2-His2 Zinc-finger Domains

Zinc-finger domains are intrinsically cell-permeable and capable of mediating protein delivery into a broad range of mammalian cell types. Here, a detailed step-by-step protocol for implementing zinc-finger technology for intracellular protein delivery is presented.

Due to their modularity and ability to be reprogrammed to recognize a wide range of DNA sequences, Cys2-His2 zinc-finger DNA-binding domains have emerged ...

Identification of Intracellular Signaling Events Induced in Viable Cells by Interaction with Neighboring Cells Undergoing Apoptotic Cell Death

Cells dying by apoptosis, also referred to as regulated cell death, acquire multiple new activities that enable them to influence the function of adjacent live cells. Vital activities, such as survival, proliferation, growth, and differentiation, are among the many cellular functions modulated by apoptotic cells. The ability to recognize and respond to apoptotic cells appears to be a universal feature of all cells, regardless of lineage or organ of origination. However, the diversity and complexity of the response to apoptotic cells mandates that great care be taken in dissecting the signaling events and pathways responsible for any particular outcome. In particular, one must distinguish among the multiple mechanisms by which apoptotic cells can influence intracellular signaling pathways within viable responder cells, including: receptor-mediated recognition of the apoptotic cell, release of soluble mediators by the apoptotic cell, and/or engagement of the phagocytic machinery. Here, we provide a protocol for identifying intracellular signaling events that are induced in viable responder cells following their exposure to apoptotic cells. A major advantage of the protocol lies in the attention it pays to dissection of the mechanism by which apoptotic cells modulate signaling events within responding cells. While the protocol is specific for a conditionally immortalized mouse kidney proximal tubular cell line (BU.MPT cells), it is easily adapted to cell lines that are non-epithelial in origin and/or derived from organs other than the kidney. The use of dead cells as a stimulus introduces several unique factors that can hinder the detection of intracellular signaling events. These problems, as well as strategies to minimize or circumvent them, are discussed within the protocol. Application of this protocol should aid our expanding knowledge of the broad influence that dead or dying cells exert on their live neighbors, both in health and in disease.

Here, we present a protocol for the determination of intracellular signaling events induced in viable cells by physical interaction with adjacent dead or dying cells. The protocol focuses on signaling events induced by receptor-mediated recognition of the dead cells, as opposed to their phagocytic uptake or release of soluble mediators.

Cells dying by apoptosis, also referred to as regulated cell death, acquire multiple new activities that enable them to influence the function of adjacent ...

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

We present a software solution for semi-automated tracking of relative protein concentration along the length of dynamic cellular protrusions.

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex ...