Jul 2026· Molecular Systems Biology· 0 citations· 73 references
Medicine
TL;DR
A multi-omics framework that infers context-specific protein activities from transcriptomic, phosphoproteomic, and protein correlation-based protein-protein interaction data is introduced, integrating modality-specific algorithms via network diffusion.
Abstract
The state of a cell depends not only on protein abundance, but also on the biochemical and cellular activities of proteins, which are largely invisible to abundance profiling alone. Here, we introduce a multi-omics framework that infers context-specific protein activities from transcriptomic, phosphoproteomic, and protein correlation-based protein-protein interaction data, integrating modality-specific algorithms via network diffusion. Applying it to a panel of phenotypically diverse HeLa cell lines, whose genetic drift provides a natural perturbation system, we make three findings. First, physical separation of monomeric and assembled protein fractions by protein correlation profiling provides direct evidence that complex assembly buffers variation in gene copy number and transcription, a mechanism previously only inferred from bulk measurements. Second, using Let7 perturbation data, CRISPR gene dependency scores, and subcellular localization, we orthogonally validate that inferred protein activities capture functional regulation linked to cellular phenotypes inaccessible from abundance data alone. Third, differential analysis of context-specific activity profiles identifies molecular mechanisms underlying phenotypic divergence, including a WIPF1/WIPF2--Arp2/3 axis governing invadopodium formation and infection susceptibility, and an immunoproteasome switch linked to immune adaptation.
Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.
Enikő Zakar-Polyák, C. Kerepesi· bioRxiv· 0 citations
Protein phosphorylation regulates nearly every cellular process, yet most of the hundreds of thousands of human phosphosites remain functionally uncharacterized. Rather than prioritising phosphosites by conservation or structural features, here we use the abundance of an interaction partner as a readout of whether a phosphosite affects that interaction. This idea exploits the fact that subunits of stable complexes are often degraded when unbound. Here, we apply a nested linear regression model to pan-cancer data from 1,006 tumours, while controlling for transcriptional and other covariates. We identified 6,160 associations between 3,038 phosphosites and the abundance of interacting proteins, including several known interaction-regulating sites. Mapping these onto AlphaFold-predicted complexes placed 239 sites at interaction interfaces, while another 402 were linked to compartment-specific localisation, indicating that phosphorylation can also tune interactions by relocating proteins between compartments. Affinity-purification mass spectrometry of NKAP and NUF2 phosphosite mutants experimentally supported some of these predictions. Together, this framework reveals a widespread coupling between phosphorylation and interaction-dependent protein abundance and provides a prioritized, structure-informed resource for characterizing the human phosphoproteome
The results indicate that GmGM provides a unified, reproducible framework for joint cell clustering and gene-network inference, capable of revealing cellular structure beyond that captured by conventional pipelines.
O. Lanzetta, L. Cutillo, Bailey Andrew et al.· 0 citations
Proteins on the cell surface do not act in isolation, rather, their spatial organization and physical interactions are key determinants of the cellular function.
Using DNA-barcoded antibodies and proximity-dependent ligation, PNA simultaneously measures the abundance, clustering, and colocalization of 155 surface proteins, generating nanoscale surface maps comprising ∼50,000 molecular positions per cell without the use of optics.
This spatially resolved readout enables systematic analysis of membrane protein networks across thousands of cells. We demonstrate the utility of PNA by identifying the proxiome of the CD19 CAR receptor at steady state and revealing dynamic proteomic remodeling during tumor cell encounter, including key phenomena such as trogocytosis and cell—cell conjugate formation. By integrating spatial context with multiplex protein profiling at scale, PNA provides a powerful platform for protein interactomics, biomarker discovery, and mechanistic insights across immunology, oncology, and cell therapy research.
By integrating spatial context with multiplex protein profiling at scale, PNA provides a powerful platform for protein interactomics, biomarker discovery, and mechanistic insights across immunology, oncology, and cell therapy research.
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Technological Innovations in Immunology (TECH)
W. Love, Hanna van Ooijen· Journal of Immunology· 0 citations
Protein self-interaction to form homodimers and higher-order homo-oligomers is a ubiquitous phenomenon fundamental to living organisms. Recent structural, system-level, and computational insights reveal that self-interacting proteins dictate the specificity, rewiring, and topological complexity of cellular signaling networks and macromolecular assemblies. Beyond their physiological roles, aberrant or dysregulated homotypic interactions disrupt cellular proteostasis, driving the formation of toxic non-native oligomers, pathological amyloid fibrillization, or aberrant liquid–liquid phase separation transitions linked to neurodegenerative and systemic diseases. This review provides a comprehensive overview of the state-of-the-art experimental methodologies, including proximity labeling, as well as the advanced computational frameworks, such as deep learning architectures and protein language models, used to map the structural dynamics of SIPs. Furthermore, we dissect the evolutionary trajectories of SIPs within protein–protein interaction networks, which are underpinned by dosage-balance constraints, and highlight their diverse functional advantages, ranging from allosteric modulation to biomolecular condensation. Finally, we summarize the molecular mechanisms linking pathological self-associations to human disorders, underscoring the emerging paradigm of targeting homotypic interfaces as a promising frontier for precision therapeutics.
Yuanxiao Gao, Wenyu Zhang, Guang Hu· International Journal of Mol...· 0 citations
Protein-protein interactions (PPIs) are fundamental to cellular signaling networks, yet many remain undetected due to technical limitations of individual affinity purification approaches. To address this, we systematically mapped the interaction landscapes of six regulatory proteins involved in cell proliferation, immunity, and inflammation, including three transcription factors (TFs) and three kinases. We implemented an integrated proteomics workflow that combined four complementary affinity purification strategies: native immunoprecipitation, two crosslinking-assisted capture methods, and proximity labeling. Combining these approaches revealed distinct yet overlapping interaction profiles, uncovered numerous previously unreported interactors not reliably detected by individual methods, and robustly recovered known interactions while substantially extending PPI networks. Despite method-specific differences at the protein level, functional enrichment analyses showed strong convergence on coherent biological pathways. Biochemical approaches validated most of the previously unreported interactions, including putative weak and transient complexes stabilized by crosslinking. Functional assays revealed a previously unrecognized physical interaction between FOXA1 and PBX1 TFs and demonstrated their cooperative regulation of transcriptional programs and cell fitness in estrogen receptor (ER) positive breast cells. We propose that the FOXA1-PBX1 complex could represent a higher-order regulatory node integrating chromatin accessibility and ER-driven transcriptional output. HIGHLIGHTS Complementary affinity purification strategies uncover putative weak and transient protein-protein interactions Functional pathway convergence validates biologically coherent interactome expansion Biochemical validations confirm unreported interactions Functional validation studies identify a FOXA1-PBX1 pioneer factor complex that regulates estrogen receptor transcriptional programs Graphical Abstract
