Affinity Purification-Mass Spectrometry (AP-MS) is a powerful technique for studying protein interactions. It combines specific protein isolation with high-throughput mass spectrometry to identify and quantify protein complexes in cells.

AP-MS offers advantages like detecting weak interactions and discovering new partners. However, it can produce false positives and struggles with some protein types. Careful data analysis is crucial to extract meaningful biological insights from AP-MS experiments.

Affinity Purification-Mass Spectrometry (AP-MS) Principles and Applications

Principles of AP-MS workflow

• Isolates protein complexes through specific interactions leveraging affinity between target protein and binding partner
• Workflow steps:
  1. Express tagged bait protein in cells or organisms
  2. Lyse cells and extract proteins
  3. Purify bait protein and associated complexes using affinity
  4. Elute purified complexes
  5. Digest proteins (typically with trypsin)
  6. Analyze using mass spectrometry
     • Ionize peptides
     • Measure mass-to-charge ratio
     • Fragment peptides
     • Search databases to identify proteins
• Quantification methods analyze protein abundance
  • Label-free quantification compares unlabeled samples
  • Isotope labeling techniques incorporate heavy isotopes (SILAC, iTRAQ)

Types of AP-MS affinity tags

• Epitope tags small peptide sequences recognized by antibodies
  • FLAG tag hydrophilic octapeptide easily detected
  • HA tag derived from influenza hemagglutinin widely used
  • c-Myc tag from c-Myc oncogene versatile option
• Protein tags larger fusion partners with specific properties
  • GST larger tag may affect protein function but high affinity
  • MBP enhances solubility useful for difficult proteins
  • His-tag small metal-binding tag allows purification on metal columns
• Enzymatic tags engineered enzymes for covalent labeling
  • HaloTag engineered dehalogenase forms covalent bond with ligand
  • SNAP-tag engineered O6-alkylguanine-DNA alkyltransferase labels proteins in living cells
• Biotin-based tags exploit strong biotin-streptavidin interaction
  • Avi-tag allows biotinylation in vivo or in vitro
  • Bio-tag enables biotinylation in vivo for stable complexes

AP-MS vs other methods

• Advantages of AP-MS:
  • Identifies protein-protein interactions high-throughput manner
  • Detects weak or transient interactions often missed by other methods
  • Discovers novel interaction partners unbiased approach
  • Applies to various biological systems versatile technique
  • Provides quantitative information on interactions strength and dynamics
• Limitations of AP-MS:
  • Generates false positives due to non-specific binding requires careful controls
  • Misses interactions sensitive to cell lysis conditions may disrupt some complexes
  • Overexpression of bait protein leads to artificial interactions need physiological levels
  • Struggles with low-abundance proteins may require enrichment strategies
  • Challenges in studying membrane proteins or insoluble complexes requires specialized protocols

Analysis of AP-MS data

• Data preprocessing prepares raw data for analysis
  • Detect and align peaks across samples
  • Identify peptides based on mass spectra
  • Infer proteins from peptide data
• Quantification methods measure protein abundance
  • Spectral counting uses number of identified spectra
  • Intensity-based approaches use peptide ion intensities
• Statistical analysis determines significant interactions
  • Compare bait samples to controls identify enriched proteins
  • Calculate enrichment scores quantify interaction strength
  • Estimate false discovery rate (FDR) control for false positives
• Filtering strategies remove non-specific interactions
  • Abundance-based filtering removes low-abundance proteins
  • Reproducibility-based filtering keeps consistent interactions
  • Contaminant removal using databases (CRAPome) eliminates common contaminants
• Network analysis visualizes and interprets interactions
  • Visualize protein-protein interaction networks graph-based representations
  • Identify protein complexes cluster analysis
  • Integrate with existing interaction databases expand knowledge
• Functional analysis interprets biological significance
  • Gene Ontology (GO) enrichment identifies overrepresented functions
  • Pathway analysis maps interactions to known pathways
  • Protein domain analysis reveals functional modules