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Proteomic characterization of protein complexes leverages the versatile platform of liquid chromatography-tandem mass spectrometry to elucidate molecular and cellular signaling processes underlying the dynamic regulation of macromolecular assemblies. Here, we describe a complementary proteomic approach optimized for immunoisolated protein complexes. As the relative complexity, abundance, and physiochemical properties of proteins can vary significantly between samples, we have provided (1) complementary sample preparation workflows, (2) detailed steps for HPLC and mass spectrometric method development, and (3) a bioinformatic workflow that provides confident peptide/protein identification paired with unbiased functional gene ontology analysis. This protocol can also be extended for characterization of larger complexity samples from whole cell or tissue Xenopus proteomes.
Technological advances in sample preparation and instrumentation for application of mass spectrometry-based proteomics to biological samples has permitted an increasing depth of proteome coverage. For example, biochemical and analytical separation strategies, such as multidimensional chromatography (1, 2) and affinity enrichment (3, 4), have seen significant advances that include specific optimization for downstream mass spectrometry analysis. These methods are complemented by continued development of nanoscale liquid chromatography, affording improved separation efficiency (5), as well as mass spectrometry technology that provides improved mass accuracy, resolution, and ion transmission and fragmentation (6, 7). Collectively, these achievements have enabled proteomic analysis to answer diverse biological questions, ranging from large scale, discovery-based experiments (8) to targeted, hypothesis-driven studies (9–11).
Leveraging several of these technologies, this chapter describes a mass spectrometry-based proteomics workflow (Fig. 1), tailored towards the analysis of protein complexes and assemblies obtained from affinity purification strategies ((12); see Chapter 21). Complementary sample preparation strategies are described (Subheading 3.1); a gel-based approach, which is readily used for optimization experiments and advantageous for complex samples (e.g., >1,000 proteins) of high dynamic range, and a solution-based approach that employs a single dimension of separation, providing greater reproducibility and optimal for lower complexity and sample amounts (13). Peptide analysis is performed by reverse phase nanoscale liquid chromatography coupled online to tandem mass spectrometry (Subheading 3.2). Data processing and analysis comprise the greatest overall proportion (based on time) of the workflow (Subheading 3.3). This multi-step process involves filtering of MS/MS spectra, matching of experimental spectra to theoretical spectra generated from in silico digestion of a protein sequence database. Next, database search software provides putative Peptide Spectrum Matches (PSMs). Then, post-search validation algorithms calculate p-values for all PSMs (i.e., the likelihood of an incorrect match by chance) as well as q-values, which allow filtering of PSMs to achieve a desired global false discovery rate (FDR). Peptides passing these criteria are then assembled into protein groups, requiring a minimum number of unique peptides (e.g., 2) to define a protein group. Finally, bioinformatic and data reduction strategies are presented to aid in transitioning from hypothesis-generating to hypothesis-driven studies. Overall, these strategies are applicable not only to affinity-purified samples, but to a broad range of cellular and organism proteomes.
Electrophoresis and protein staining
In-gel digestion and peptide extraction
Store stock solutions in glass containers that have been thoroughly rinsed with ultrapure water. Avoid using glassware that has been washed with detergents.
Unless otherwise stated, store solutions at RT in glass containers that have been thoroughly rinsed with ultrapure water.
Unless otherwise stated, all solutions should use LC-MS grade solvents.
This protocol is performed over the course of 3 days. To minimize keratin and other environmental contaminants, it is recommended to wear a lab coat and hair protection, to avoid close contact and limit environmental exposure of the pre-case gel during sample loading and protein staining.
Electrophoresis and protein staining (Day 1)
In-gel digestion (Day 2)
Working solution volumes are calculated for 16 samples (based on 90 µL of solution/well except where noted). For higher throughput, a 96-well plate with sealing mat is recommended (see Note 10).
All solutions can be added/removed from the wells using a multichannel pipettor. For each sample set, use a different set of multichannel tips, though one set of tips can be used for all steps performed on Day 2.
Peptide extraction (Day 3)
All procedures are performed at either RT (20°C) or higher (where indicated) to avoid precipitation of detergent or urea from solutions. For this purpose, preset the temperature of the microcentrifuge to 20°C. This protocol is performed over the course of 2 days.
In-solution digestion (Day 1)
In-solution digestion (Day 2)
For global proteomic analysis of complex biological samples, data-dependent acquisition using collision-induced dissociation (CID) on a hybrid mass spectrometer, such as the LTQ-Orbitrap Velos, is the preferred method. The following steps summarize recommended instrument parameters for a hybrid LTQ-Orbitrap Velos mass spectrometer. While acquisition methods are often instrument-specific, the overall goal of the method should be to optimize the duty cycle or cycle time of the method, often de fined as the time between repeated full scan MS acquisition events or the time to accomplish a single round of peptide fragmentation events. For most nanoLC chromatographic separations, an optimal cycle time should average ≤4 s.
To date, there are several well-developed software suites that process and analyze tandem mass spectra from large-scale, bottom-up proteomic experiments and provide a list of high confidence protein identifications. An overview of this workflow is presented, and for each stage of analysis a brief description is provided below. Although the spectral filters and algorithms may differ between software, the conceptual workflow is similar. For clarity, the individual steps described below use Proteome Discoverer and Scaffold as representative software.
Peptide spectrum matching by database searching
Protein level filtering and bioinformatics analysis
1While many HPLC configurations can be used, the system should have the capacity for nanoliter per minute flow rates (ideally ≤500 nL/min). Also, the system should have dedicated nano and capillary pump capabilities that allow for shorter overall run times.
2Mobile phases should be replenished from the stock solution monthly.
3Many chemistries for column packing material are available and should be selected based on relative retention of desired analytes. For example, Magic C18 AQ resin was selected for the trap column for its increased retention of hydrophilic peptides, such as shorter length phosphopeptides. The Acclaim PepMap material was selected for the analytical column as it is available in sub-2 µm particle size. When selecting a column, instrument pressure limits should be noted as smaller particle sizes and longer columns increase operating pressures.
4While several mass spectrometer configurations are adequate for these experiments, a hybrid instrument such as the LTQ-Orbitrap Velos offers (1) resolution up to 100,000, (2) <2 ppm mass accuracy (external calibration) in the FT Orbitrap detector, and (3) attomol sensitivity in the linear ion trap detector.
5While a computing cluster is preferred, the high initial and maintenance costs often make this option impractical. As a second option, a multi-CPU or multi-core workstation can be purchased for about $5,000. If multi-threaded applications will be used (e.g., Mascot Server), then a dual CPU system should be considered. Otherwise, maximizing the overall processing speed of a single multi-core CPU should be the goal. For a more details on hardware specifications see http://www.matrixscience.com/help/pc_specs.html.
6Currently there are many excellent options for open-source or commercial software suites that include database search engines. The most critical aspect when selecting a software suite or database search tool is the capability to accurately estimate probability values (e.g., q-values or posterior error probabilities) of individual PSMs. All software cited above satisfies this requirement.
7If loading additional eluates, skip two lanes between the samples. Reserve the remaining eluate (10%) for western blot analysis.
8For more complex samples or greater protein load, resolving proteins for entire gel length may be beneficial.
9If necessary, the stained gel can be stored in ultrapure water at 4°C, wrapped tightly, up to 2–3 weeks until proceeding to ingel digestion, though some sample loss may occur.
10Axygen microfuge tubes can be used as an alternative to a 96-well plate.
11Do not mince gel slices as this increases likelihood of pipeting gel pieces into autosampler vials during extraction.
12If samples are concentrated to dryness, add 30 µL of 50% ACN/0.5% FA solution, vortex briefly, and SpeedVac to reduce the volume as above.
13Assuming an enzyme:protein ratio of 1:50–1:100, then the working solution of trypsin is sufficient for digestion of 25–50 µg of protein. If more protein is digested then increase concentration accordingly.
14If peptide yield is known or can be estimated based on input material, concentrate sample to a final volume containing a peptide concentration of 0.5 µg/µL. Peptide concentration can be estimated by measuring A280 on a Nanodrop and dividing by 1.1 (extinction coefficient estimated based on the tryptophan frequency in mammalian proteins from a 0.1% protein solution).
15Gradient and total run times provide a starting point for method development and should be optimized for specific LC hardware configurations, chromatographic material, and sample complexity.
16For some instrument configurations, disabling the “preview scan” function may cause cycle times to routinely exceed 4 s. If this occurs, then decreasing resolution to 30,000 is recommended.
17Recommended dynamic exclusion settings for a 90 min gradient are as follows: a repeat count of 1, repeat duration of 30, exclusion list size of 500, exclusion duration 70, and exclusion mass width 10 ppm. The exclusion duration should be adjusted based on chromatographic peak widths.
18Percolator has been implemented in Mascot 2.3 and Proteome Discoverer 1.3, while PeptideProphet is available using either Scaffold (http://www.proteomesoftware.com) or the Trans-Proteomic Pipeline open-source software (http://tools.proteomecenter.org/wiki/index.php?title=Software:TPP).
19PeptideProphet and ProteinProphet probability filters should be selected to reduce FDR to less than 1% at both the peptide and protein level. While confidence thresholds can vary between datasets, a general starting point is 99.0% protein probability, 95% peptide probability, and a minimum of 2 peptides/protein. For a more detailed discussion of error rate estimation see ref. (17).
20Spectral counting analysis is a computationally simple method to evaluate which proteins may have large relative abundance differences between control and experimental samples. However, in the absence of biological replicates or independent validation assays, the statistical power of these analyses is limited and the results should be interpreted conservatively.