Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Retinitis Pigmentosa (RP) is a hereditary retinal disorder that causes the atrophy of photoreceptor rod cells. Since individual defective genes converge on the same disease, we hypothesized that all causal genes of RP belong in a complex network. To explore this hypothesis, we conducted a gene connection analysis using 161 genes attributed to RP, compiled from the Retinal Information Network, RetNet. We then examined the protein interaction network (PIN) of these genes. In line with our hypothesis, using STRING, we directly connected 149 genes out of the recognized 159 genes. To uncover the association between the PIN and the ten unrecalled genes, we developed an algorithm to pinpoint the best candidate genes to connect the uncalled genes to the PIN and identified ten such genes. We propose that mutations within these ten genes may also cause RP; this notion is supported by analyzing and categorizing the known causal genes based on cellular locations and related functions. The successful establishment of the PIN among all documented genes and the discovery of novel genes for RP strongly suggest an interconnectedness that causes the disease on the molecular level. In addition, our computational gene search protocol can help identify the genes and loci responsible for genetic diseases, not limited to RP.

NRF2 (NFE2L2) is a cytoprotective transcription factor associated with >60 human diseases, adverse drug reactions and therapeutic resistance. To provide insight into the complex regulation of NRF2 responses, 1962 predicted NRF2-partner interactions were systematically tested to generate an experimentally defined high-density human NRF2 interactome. Verification and conditional stratification of 46 new NRF2 partners was achieved by co-immunoprecipitation and the novel integration of quantitative data from dual luminescence-based co-immunoprecipitation (DULIP) assays and live-cell fluorescence cross-correlation spectroscopy (FCCS). The functional impact of new partners was then assessed in genetically edited loss-of-function (NRF2-/-) and disease-related gain-of-function (NRF2T80K and KEAP1-/-) cell-lines. Of the new partners investigated >77% (17/22) modified NRF2 responses, including partners that only exhibited effects under disease-related conditions. This experimentally defined binary NRF2 interactome provides a new vision of the complex molecular networks that govern the modulation and consequence of NRF2 activity in health and disease.