Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry

Abstract

Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Current efforts have mainly focused on generating reliable, reproducible, and high confidence protein interaction data sets for functional characterization. The availability of increasing amounts of information on protein interactions in eukaryotic systems and new bioinformatics tools allow functional analysis of quantitative protein interaction data to unravel the biological significance of the identified protein interactions. Existing studies in this area have laid a solid foundation toward generating a complete map of in vivo protein interaction networks of protein complexes in cells or tissues.

Fig. 1.

AP-QMS strategies for studying protein interaction networks of protein complexes. A, PAM AP-QMS under native conditions. PAM-SILAC is displayed; however, ICPL or ICAT can be incorporated at the protein level as indicated by the dotted line. B, MAP AP-QMS under native conditions: MAP-SILAC (I) and MAP coupled with chemical labeling (II), i.e. MAP-ICPL/ICAT or MAP-iTRAQ as indicated by dotted lines. C, label-free AP-QMS. D, QTAX MS in which HB tag-based tandem affinity purification is carried out under fully denaturing conditions. L, medium containing light forms of selected amino acids; H, medium containing heavy forms of selected amino acids; Sample, cells expressing a tagged subunit; Control, wild type cells without a tagged protein. During quantitation analysis, three groups of proteins are often identified: 1) protein complex subunits (only present in the tagged sample and not detected in the control), 2) protein complex-interacting proteins (relative abundance ratio of light versus heavy forms >1), and 3) background proteins (relative abundance ratio of light versus heavy forms ∼1).

Fig. 2.

Quantifying dynamic interacting proteins of human 26 S proteasome complex with combined Tc-PAM-SILAC and MAP-SILAC approaches. 293 cells stably expressing Rpn11-HTBH (i.e. 293^Rpn11-HTBH) were grown in light medium, whereas 293 cell stably expressing HTBH tag alone (i.e. 293^HTBH) were grown in heavy medium used as a control. Proteasome complexes were purified by binding to streptavidin beads followed by tobacco etch virus cleavage elution. The Tc-PAM-SILAC experiment consisted of three independent PAM-SILAC experiments with varied incubation times (i.e. 2 h, 1 h, and 20 min) in which proteasome complexes were purified after mixing the two compared samples (293^Rpn11-HTBH versus 293^HTBH). The MAP experiment was carried out with optimized incubation time at 2 h in which the purifications of proteasome complexes were done separately for the two compared samples (293^Rpn11-HTBH versus 293^HTBH) and then mixed for comparison. Representative TOF MS spectra of peptides derived from two different types of proteins identified in Tc-PAM-SILAC (A1, B1, and C1; A2, B2, and C2; and A3, B3, and C3) and MAP-SILAC (A4, B4, and C4) experiments are shown. ○ and ● represent the light and heavy forms of the peptide, respectively. The SILAC ratios (light/heavy) for each peptide are shown in the corresponding spectra. A1–A4, TOF MS spectra of a tryptic peptide (MH₂²⁺ 807.90, AFYPEEISSMVLTK) matched to Hsp70-1, a stable PIP. Its SILAC ratios, which can be identified unambiguously using both methods, remain the same in Tc-PAM-SILAC and MAP-SILAC. B1–B4, TOF MS spectra of a tryptic peptide (MH₂²⁺ 766.39, acetyl-TTSGALFPSLVPGSR) matched to ADRM1/hRpn13, a dynamic PIP that has increased SILAC ratios in Tc-PAM-SILAC when incubation time is decreased. It was unambiguously identified as a specific PIP by MAP-SILAC. C1–C4, TOF MS spectra of a tryptic peptide (MH₃³⁺ 710.75, QIIQQNPSLLPALLQQIGR) matched to hHR23B, a dynamic PIP that has a very fast on/off rate and has no change in SILAC ratios when the incubation time changes in Tc-PAM-SILAC. It was only identified as a specific PIP using MAP-SILAC. The figure is adapted from Ref. .

Fig. 3.

General data analysis work flow for validating and determining biological significance of identified protein interactions of protein complexes using AP-QMS strategies. Using experimentally or statistically set high confidence threshold values, quantitative data obtained from AP-QMS experiments should be partitioned such that all proteins quantified below the quantitative threshold cutoff are considered as nonspecific background and discarded from further analysis, and all proteins quantified above the threshold cutoff are considered as specific and high confidence interactors for subsequent data analysis steps. Once a high confidence interaction data set is obtained, combinations of protein interaction network mapping, clustering analysis, network topology, and functional enrichment can be carried out. Interaction network mapping is typically accomplished by extracting protein-protein interaction data form PPI databases to be visualized by bioinformatics tools. Clustering analysis needs at least three data sets, whereas network topology analyses require higher numbers of AP-QMS data sets, and therefore both clustering analysis and network topology are only suitable with certain experimental designs. Functional enrichment analysis involves extracting gene annotation data (GO terms, protein domains, expression, pathways, homology, complexes, etc.) from public databases.