DIA-MS to decode in vivo protein-protein interactions

Most proteins interact with each other and function in the form of complexes (1). Determinization of these protein-protein interactions (PPIs) in vivo thus plays an essential role in understanding their regulation and coordination (2). For different purposes, three DIA-MS implementations are commonly applied to PPI studies.

AP-DIA-MS Affinity purification (AP) coupled with MS (3) uses the proteins of interest as the “bait” and collects their binding partners (“preys”) for identification. In 2013, Lambert et al. and Collins et al. designed an AP-DIA-MS setting (2, 4), wherein DIA was realized with SWATH-MS, and manually analyzed the data with a library-based, peptide-centric approach. This method has been used to study interactome differences under internal (gene mutation) and external (drug treatment) perturbations, respectively (2). Remarkably, 1,967 proteins with a quantification dynamic range of over four orders of magnitude could be profiled in the temporal interactomes of 14-3-3β system (4). Later, AP-DIA-MS has seen updates from technical support (5) expansions to several biomedical applications, including targeting tumor markers (6) and determining drug affinity responsive and its target stability (7). However, AP-MS cannot be used when the targets are unknown.

XL-DIA-MS In cross-linking (XL)-MS, an analyte can either belong to the targeted protein complexes or the rest of the untargeted proteome. A chemical cross-linker is added to the analyte and covalently links adjacent amino acid residues before MS measurement (8). The topological information provided by XL-MS can be used to reconstruct the spatial structure of PPIs (9). Muller et al. benchmarked XL-DIA-MS with seven chemically cross-linked proteins (10) and found that DIA-MS provides higher accuracy in quantitation and better reproducibility than DDA-MS. Additionally, this method was further coupled with photoactivatable XL to study pH-dependent conformational changes of a protein complex (11). The main limitation of XL-DIA-MS is that the reported interactions are biased towards high abundant proteins, compromising the identification coverage (1).

SEC-DIA-MS Size exclusion chromatography (SEC) separates the analytes depending on their relative size and hydrodynamics (12). SEC-MS is thus an untargeted approach that preserves the native properties of system-wide protein complexes (13). Heusel et al. applied SWATH-MS to acquire interactomics information from HEK293 lysates (14). The algorithm CCprofiler was then developed to infer and quantify proteins and protein complexes. The algorithm for the latter process was termed “complex-centric” because, similarly to the peptide-centric data search, it also involves a targeted data extraction step to derive elution profiles of complex subunits based on an a priori PPI database. Results showed that 55% of the detected protein mass is in the form of protein complexes entailing 462 complexes and 2,127 protein subunits. An alternative approach termed “network-centric” evaluates the edges (relations) rather than the points (proteins) or the clusters (protein complexes) in the PPI network (15). The network-centric software SECAT was applied to compare PPI differences between cell cycle states and provided interactomics alterations with a higher resolution. Comparatively, the network-centric approach is better suited to study phenotype-associated molecular mechanisms (15, 16). In addition, the recent software PCprophet (17) enables de novo PPI-free protein complex predictions. Bearing high potential, over 1000 complexes composed of over 10,000 PPIs could be retrieved from the HeLa cell line.

Several alternative MS-based approaches are also popularly used to characterize PPIs and complex structures and can potentially be integrated with DIA-MS. For example, thermal proteome profiling measures PPI thermal stability and abundance (18), while MS footprinting uses hydrogel radiation reactions to characterize the PPI domains at different conditions (19). An additional potential of DIA-MS-based PPI studies might be the meta-analysis of full proteome co-variation, which can collect data to identify novel protein complexes and PPIs across species and individuals. As an initial test, Stalter et al. analyzed protein complex co-variations using the SWATH-MS profiles of four species (20). They found that homomeric paralogues display a unique co-expression pattern distribution.

Schematic of DIA-MS-based PPI applications


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