Combined proximity labeling and affinity purification−mass spectrometry workflow for mapping and visualizing protein interaction networks

Abstract

Affinity purification coupled with mass spectrometry (AP-MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP−MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP-MS and BioID data has been shown to comprehensively characterize a protein's molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP-MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform (http://proteomics.fi) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP-MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d.

Small mitochondrial protein NERCLIN regulates cardiolipin homeostasis and mitochondrial ultrastructure

“…3d). Using the BioID data and a previously described algorithm for mapping of protein localization 23,24 , we confirmed that similarly to GRPEL1 and GRPEL2, NERCLIN localizes to the mitochondrial matrix ( Fig. 3e).…”

“…The mass spec analysis was done in technical duplicates. The mass spectrometry data were analyzed as previously described 20,24 .…”

SARS‐CoV‐2–host proteome interactions for antiviral drug discovery

“…In addition, a comparison of the results with previous publications (Gordon et al , 2020a ; Zhang, Cruz‐cosme, et al , 2020 ; Lee et al , 2021 ) showed that in most cases there was no obvious difference observed in the subcellular localization of individual ORFs in several transfected cell lines (Dataset EV7 ). To further characterize the potential compartment specificity of viral ORFs, we employed our developed MS‐microscopy system (Liu et al , 2020 ), which uses a quantitative interactome profile to map the cellular distribution of the bait protein (Fig EV4A and Dataset EV7 ). This revealed two baits (NSP16 and ORF3a) that were associated with endosomes, five baits (S, E, ORF7b, ORF8, and ORF10) that showed an ER distribution (Fig EV4A ), and four baits (M, ORF6, ORF7a, and NSP10) that were related to the Golgi apparatus.…”

Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector–Host Protein–Protein Interactions

“…Creative approaches using the combined power of AP-MS and BioID have enabled diversion of relative spatial distances for proteins within a complex [101,122]. Liu et al introduced this principle using a MAC-tag (StrepIII-BirA*), enabling the integration of stoichiometric complexation information from AP-MS with the identification of transient or proximal interactions by BioID.…”