Home › Immunology › Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics
Immunology JoVE (Open Access) Citable · DOI

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

DOI: 10.3791/51656-v
What you'll learn
  • ✓ Perform SILAC-based immunoprecipitation to identify protein interaction partners
  • ✓ Distinguish true interactions from contaminants using quantitative proteomics
  • ✓ Analyze and filter mass spectrometry data for high-confidence protein interactions
Protocol

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.

Difficulty
advanced
Total time
~3–5 days (cell labeling, lysis, IP, MS analysis, and bioinformatic processing)
Model organism
Mammalian cell culture (species-agnostic; e.g., HEK293, CHO)
Biosafety
BSL-1

Steps

1
Harvest SILAC-labeled cell lysates

Collect cells cultured in heavy and light SILAC media, lyse them, and prepare whole-cell lysate for immunoprecipitation. This creates differentially labeled protein pools for quantitative comparison.

▶ 01:49
2
Bind lysate to anti-GFP affinity beads

Incubate cell lysate with anti-GFP beads to capture the bait protein (GFP-tagged target) and associated binding partners. Use less-stringent wash conditions to preserve low-affinity interactions.

▶ 03:39
3
Prepare samples for mass spectrometry

Elute bound proteins from beads, denature, digest with protease, and prepare peptide samples for liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis.

▶ 04:45
4
Remove low-confidence peptide identifications

Filter raw mass spectrometry data to eliminate peptides with poor match quality or low spectral intensity, retaining only high-confidence identifications for downstream analysis.

▶ 06:11
5
Select high-confidence protein interactions

Apply quantitative SILAC ratio thresholds and statistical cutoffs to distinguish true interacting partners from background contaminants and non-specific binders.

▶ 07:36
6
Merge and consolidate replicate datasets

Combine results from independent replicate experiments to identify consistently detected interactions and improve statistical confidence in final protein interaction partners.

▶ 08:58
7
Interpret translation initiation factor interactions

Review case-study results showing validated protein interactions for a translation initiation factor, demonstrating the power of SILAC-IP for functional protein network mapping.

▶ 10:59
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