Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks

Abstract

Understanding how proteins interact is crucial to understanding cellular processes. Among the available interactome mapping methods, co-elution stands out as a method that is simultaneous in nature and capable of identifying interactions between all the proteins detected in a sample. The general workflow in co-elution methods involves the mild extraction of protein complexes and their separation into several fractions, across which proteins bound together in the same complex will show similar co-elution profiles when analyzed appropriately. In this review we discuss the different separation, quantification and bioinformatic strategies used in co-elution studies, and the important considerations in designing these studies. The benefits of co-elution versus other methods makes it a valuable starting point when asking questions that involve the perturbation of the interactome.

Keywords: Protein-protein interactions; SILAC; bioinformatics; chromatography; co-elution; co-fractionation; complexes; interactome; mass spectrometry; protein complex analysis; protein correlation profiling; separation technologies.

Graphical abstract

Fig. 1.

Schematic of the steps commonly involved in co-elution methods. A, General workflow of a co-elution experiment. The lysed sample containing protein complexes under native conditions is separated in a set number of fractions. Proteins from the same complex show the same co-elution profile after a bioinformatic analysis to extract an interactome map. B, Co-elution data representative from an experiment where lysed HeLa cells were fractionated by size exclusion chromatography (SEC), quantified by MS/MS, and finally used to construct an interactome network through data analysis.

Fig. 2.

Bioinformatic analysis of co-elution data. A, Bioinformatic analysis of co-elution data is complicated by the number of potential interactions. In contrast to techniques such as Y2H that find interactions between tagged proteins (“Bait-to-bait”) or BioID (and sometimes AP-MS) that find interactions involving at least one bait protein (“Bait-to-all”), co-elution experiments have the potential to find interactions between all identified proteins in a sample (“All-to-all”). B, Schematic of classifier-based analysis of co-elution data. The strength of co-elution is quantified for every pair of proteins using multiple metrics (“features”). Features derived from external data can be included, such as co-citation or co-expression. Using a gold standard set of known complexes, a subset of the protein pairs are labeled as interacting or not-interacting. Finally, a classifier uses to the features and labels to assign every pair of profiles a classifier score, to which a threshold is applied. C, Performance of single co-elution features. Interactomes were predicted from four data sets using a single co-elution metric. Each dot represents an interactome from one replicate, and the y axis gives the precision of the 500 best-scoring interactions. Interactomes were predicted using PrInCE with default parameters (CORUM gold standard). weighted_xcorr: Weighted cross-correlation, measured with R function wccsom. pearson_R_cleaned: Pearson correlation (cleaned profiles). mutual_info: Mutual information. co_apex: Mininum number of fractions between fitted Gaussian centers. pearson_P: Pearson correlation (raw profiles) p value. pearson_R_raw: Pearson correlation (raw profiles). euclidean_distance: Euclidean distance (cleaned profiles). co_peak: Number of fractions between maximum value. pearson_plus_poisson: Pearson R (raw profiles) plus Poisson noise. co_fraction: 1 if maximum values are in the same fraction, 0 otherwise. Jaccard: Overlapping fractions in which both proteins are quantified, measured with Jaccard. Data sets: 1 Kristensen et al. 2012 (20), 2 Scott et al. 2017 (29), Carlson et al. 2019 (42), Scott et al. 2015 (78).