PROMISed: A novel web-based tool to facilitate analysis and visualization of the molecular interaction networks from co-fractionation mass spectrometry (CF-MS) experiments

Abstract

Co-fractionation mass spectrometry (CF-MS)-based approaches enable cell-wide identification of protein-protein and protein-metabolite complexes present in the cellular lysate. CF-MS combines biochemical separation of molecular complexes with an untargeted mass-spectrometry-based proteomics and/or metabolomics analysis of the obtained fractions, and is used to delineate putative interactors. CF-MS data are a treasure trove for biological discovery. To facilitate analysis and visualization of original or publically available CF-MS datasets, we designed PROMISed, a user-friendly tool available online via https://myshiny.mpimp-golm.mpg.de/PDP1/ or as a repository via https://github.com/DennisSchlossarek/PROMISed . Specifically, starting with raw fractionation profiles, PROMISed (i) contains activities for data pre-processing and normalization, (ii) deconvolutes complex fractionation profiles into single, distinct peaks, (iii) identifies co-eluting protein–protein or protein–metabolite pairs using user-defined correlation methods, and (iv) performs co-fractionation network analysis. Given multiple CF-MS datasets, for instance representing different environmental condition, PROMISed allows to select for proteins and metabolites that differ in their elution profile, which may indicate change in the interaction status. But it also enables the identification of protein–protein and protein–metabolite pairs that co-elute together across multiple datasets. PROMISed enables users to (i) easily adjust parameters at each step of the analysis, (ii) download partial and final results, and (iii) select among different data-visualization options. PROMISed renders CF-MS data accessible to a broad scientific audience, allowing users with no computational or statistical background to look for novel protein–protein and protein–metabolite complexes for further experimental validation.

Rewiring of the protein–protein–metabolite interactome during the diauxic shift in yeast

“…S4–S5). Elution profiles were normalized, deconvoluted and correlated using PROMISed, a novel web-based tool to facilitate analysis and visualization of the molecular interaction networks from (CF-MS) experiments [ 55 ].

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“…B Number of differentially fractionating small molecules (upper panel) and proteins (lower panel) between the growth phases. Differentially fractionating small molecules were identified using the dis-elution score (light grey, [ 55 ] and differences in presence/absence (dark grey). For proteins, a simplified approach was used comparing the Manhattan distance of a protein to the median Manhattan distance of all proteins between two growth phases (see Materials and Methods).…”

“…Since the fractionation profile of a metabolite is dependent on its protein interaction partners, a change in PMIs between the growth phases would be reflected in the metabolite´s profile. We therefore used a statistical workflow for the pairwise comparison of fractionation profiles, dubbed dis-elution score [ 55 ] as well as the presence or absence of a metabolite in a given growth phase, to identify metabolites whose fractionation profiles differ across the examined growth phases (Fig. 3 B, Tables S7, S9).…”

“…4 B), specifically 13 of the 14 core particle (CP), and 18 of the 21 regulatory particle (RP) subunits. To learn about the re-arrangement of the proteasome across the diauxic shift transition we used PROMISed [ 55 ] to create interaction networks restricted to proteasomal subunits and co-fractionating metabolites (PCC > 0.7) (Fig. 4 D).…”

Native size exclusion chromatography-based mass spectrometry (SEC-MS) identifies novel components of the Heat Shock Protein 90-dependent proteome

“…With more studies employing SEC-MS and other cofractionation-based MS approaches, we expect our dataset (e.g., Table S2) to continue being of use as newer analysis pipelines, software, and best-practices are developed (Schlossarek et al, 2021; Pang et al, 2020; Serwetnyk and Blagg, 2021). We also hope our study lays the foundation for other cuttingedge mass spectrometric approaches that increase resolution of treatment-induced changes to protein complexes or organellar proteomes, including SEC-SWATH-MS (Heusel et al, 2020), the various LOPIT techniques (Geladaki et al, 2019; Mulvey et al, 2017), and Dynamic Organellar Maps (Itzhak et al, 2016).…”