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Results for “affinity proteomics”

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At least 19 recordsLinked to original sources

PubMed2024

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology.

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Humans↗

PubMed2026

An Arabidopsis Protein-Flavonoid Interactome Identifies Peroxiredoxin A as a Candidate for Flavonoid Action in Chloroplasts.

The ability of phytochemicals to act as small molecule effectors of protein function is a largely overlooked dimension of plant biochemistry. This is particularly true for the ubiquitous flavonoids where, despite abundant examples of functional interactions with human proteins, biological activities in plants are primarily attributed to ROS scavenging. We used affinity capture to explore the protein interactome of the flavonoid glycoside, rutin, in Arabidopsis seedlings. Unexpectedly, the 397 high-confidence candidates included numerous proteins associated with chloroplasts, where flavonoids are present at exceedingly low levels. Intriguingly, several identified targets are conserved with known flavonoid-interacting proteins in mammals, where the bioavailability of flavonoids is similarly low. Using one of these, the Arabidopsis plastidial 2-cys peroxiredoxin A, as a test case, this study substantiated the potential of affinity proteomics for identifying novel protein targets of phytochemicals and suggests that flavonoids modulate protein function in plants to a larger extent than previously suspected.

Arabidopsis↗

PubMed2025

Serum proteomics reveals high-affinity and convergent antibodies by tracking SARS-CoV-2 hybrid immunity to emerging variants of concern.

The rapid spread of SARS-CoV-2 and its continuing impact on human health has prompted the need for effective and rapid development of monoclonal antibody therapeutics. In this study, we investigate polyclonal antibodies in serum and B cells from the whole blood of three donors with SARS-CoV-2 immunity to find high-affinity anti-SARS-CoV-2 antibodies to escape variants. Serum IgG antibodies were selected by their affinity to the receptor-binding domain (RBD) and non-RBD sites on the spike protein of Omicron subvariant B.1.1.529 from each donor. Antibodies were analyzed by bottom-up mass spectrometry, and matched to single- and bulk-cell sequenced repertoires for each donor. The antibodies observed in serum were recombinantly expressed, and characterized to assess domain binding, cross-reactivity between different variants, and capacity to inhibit RBD binding to host protein. Donors infected with early Omicron subvariants had serum antibodies with subnanomolar affinity to RBD that also showed binding activity to a newer Omicron subvariant BQ.1.1. The donors also showed a convergent immune response. Serum antibodies and other single- and bulk-cell sequences were similar to publicly reported anti-SARS-CoV-2 antibodies, and the characterized serum antibodies had the same variant-binding and neutralization profiles as their reported public sequences. The serum antibodies analyzed were a subset of anti-SARS-CoV-2 antibodies in the B cell repertoire, which demonstrates significant dynamics between the B cells and circulating antibodies in peripheral blood.

Humans↗

PubMed2025

Unraveling Plant Nuclear Envelope Composition Using Proximity Labeling Proteomics.

The nuclear envelope (NE) defines the eukaryotic cell and functions in a myriad of fundamental cellular processes including but not limited to signal transduction, lipid metabolism, chromatin organization, and nucleocytoplasmic transportation. Although the general structure of the NE is well-conserved across eukaryotic kingdoms, its composition and functions vary substantially between species and remain largely unknown in plants. In this chapter, we describe a proximity-labeling-based proteomic approach to profile novel NE components in the model organism Arabidopsis. This method is generally suitable for the identification of protein components in subcellular compartments or protein complexes that are poorly accessible to traditional mass spectrometry approaches and can be easily applied to other plant species. In addition to giving a step-by-step detailed description of the proximity labeling proteomics procedure in plant samples, we also provide guidelines on the appropriate use of controls and statistical analysis to achieve a highly specific selection of probed candidates.

Proteomics↗

PubMed2026

Comprehensive proximity proteomics expand the known interactome of the oncoprotein β-catenin.

The oncoprotein β-catenin has critical roles in cell adhesion and cell signalling. β-catenin affects human physiology and pathology through numerous interaction partners, of which many have been discovered by standard affinity purification-based proteomics. However, the interaction landscape of β-catenin remains incompletely understood, highlighting a need for new experimental approaches for the exploration of β-catenin biology. Proximity proteomics, which facilitate the discovery of molecules vicinal to proteins-of-interest by mass spectrometry, have recently emerged as a powerful complementary tool for the study of protein-protein interactions, but have not been applied to β-catenin so far. We investigated the interactome of β-catenin in model cell lines by proximity proteomics using expression constructs with the biotin ligases BioID and TurboID. Mass spectrometry analyses following biotin labelling identified numerous candidate interactors of β-catenin, including many that had not been observed in earlier studies using standard proteomics. Enrichment analyses suggested that proximity proteomics capture proteins associated with the known biological functions of β-catenin, including cell adhesion, Wnt/β-catenin signalling, and transcription regulation. The molecular tools and data generated in this study provide new insights into β-catenin biology and highlight potential new regulators of β-catenin function that warrant further exploration.

beta Catenin↗

PubMed2025

Proximity Proteomics to Profile Ebola Virus Protein Interactome in Its Functional Context.

Proximity labeling-based proteomics (proximity proteomics) has emerged as a popular and versatile approach to illuminate the molecular interactions between viruses and their hosts. In this approach, a proximity labeling enzyme tag is fused to a bait protein and labels neighboring proteins with a chemical handle such as biotin, allowing for downstream affinity purification. Compared to another widely used technique, affinity purification coupled mass spectrometry, proximity proteomics enables the detection of low affinity or transient interactors that might have important functions in the viral life cycle. Further, proximity proteomics can identify interactors of a labile bait protein, of which affinity purification is technically challenging. Here, we describe a proximity proteomic protocol to identify cellular interactors of the Ebola virus polymerase. A similar strategy is readily applicable to elucidate the virus-host interactions for Marburg virus.

Ebolavirus↗

PubMed2025

High-affinity CD16A polymorphism associated with reduced risk ofsevere COVID-19.

CD16A is an activating Fc receptor on NK cells that mediates antibody-dependent cellular cytotoxicity (ADCC), a key mechanism in antiviral immunity. However, the role of NK cell-mediated ADCC in SARS-CoV-2 infection remains unclear, particularly whether it limits viral spread and disease severity or contributes to the immunopathogenesis of COVID-19. We hypothesized that the high-affinity CD16AV176 polymorphism influences these outcomes. Using an in vitro reporter system, we demonstrated that CD16AV176 is a more potent and sensitive activator than the common CD16AF176 allele. To assess its clinical relevance, we analyzed 1,027 patients hospitalized with COVID-19 from the Immunophenotyping Assessment in a COVID-19 cohort (IMPACC), a comprehensive longitudinal dataset with extensive transcriptomic, proteomic, and clinical data. The high-affinity CD16AV176 allele was associated with a significantly reduced risk of ICU admission, mechanical ventilation, and severe disease trajectories. Lower anti-SARS-CoV-2 IgG titers were correlated to CD16AV176; however, there was no difference in viral load across CD16A genotypes. Proteomic analysis revealed that participants homozygous for CD16AV176 had lower levels of inflammatory mediators. These findings suggest that CD16AV176 enhances early NK cell-mediated immune responses, limiting severe respiratory complications in COVID-19. This study identifies a protective genetic factor against severe COVID-19, informing future host-directed therapeutic strategies.

Humans↗

PubMed2025

Hepatocyte proteome destabilization and novel targets for PFASs unveiled through combined thermal proteome profiling and deep transfer learning.

Identifying protein targets for per- and polyfluoroalkyl substances (PFASs) is essential to understand their toxicity and health risks. However, knowledge about their interacting proteins is limited since reliable identification methods are lacking. We developed an integrated approach combining thermal proteome profiling (TPP) and deep transfer learning (DTL) modeling to efficiently identify cellular targets of PFAS. TPP measured PFAS binding proteins and the affinities by nanospray liquid chromatography tandem mass spectrometry, while DTL models were constructed to predict PFAS-protein affinities using neural network algorithms. TPP results revealed that PFASs uniquely destabilized the proteome of HepG2 cells, unlike the stabilizing effects by other xenobiotics. Key protein targets for three representative PFASs (PFOA, GenX and Novec 649) were identified, which exhibited weak binding affinities (median EC50 ≈ 30 μM). The number of protein targets increased with molecular weights among the three PFASs. The DTL model achieved a higher Pearson correlation coefficient of 0.89, and reduced mean squared errors by 54 % over previous models for drug-protein interactions. Notably, TPP and DTL jointly pinpointed ribosomal proteins as novel targets of GenX, potentially linking it to cell apoptosis through disrupted protein synthesis. Biolayer interferometry validated GenX binding to RPL4 protein, driven by electrostatic interactions and halogen bonds. This integrated approach effectively uncovers novel PFASs targets, advancing insights into their adverse health effects.

Humans↗

PubMed2025

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

deep learning↗

PubMed2026

Paradoxical non-catalytic kinase functions are driven by inhibitor-induced displacement of autoinhibitory domains.

ATP-competitive kinase inhibitors represent one of the largest classes of targeted anti-cancer drugs. While their primary mechanism is to block catalytic activity, they can also trigger paradoxical phenotypic effects that cannot be explained by catalytic inhibition alone. These observations point to a hidden layer of drug action that modulates non-catalytic kinase functions via changes in kinase conformation and protein-protein interactions (PPIs). Here, we developed a multimodal proteomics approach combining limited proteolysis coupled mass spectrometry on affinity-purified samples (AP-LiP-MS), AP-MS, and proximity labeling-MS to map inhibitor-induced conformation and PPI changes. We show that inhibitor binding causes structural rearrangements in the autoinhibitory domains (AIDs) of all tested kinases, consistent with a transition to an open, active-like kinase conformation. These structural shifts drive distinct kinase-protein interaction changes that control non-catalytic functions: sequestration of AMPK by inhibited CAMKK2 blocks phosphorylation by other kinases, CHEK1 inhibition causes dissociation from the mitochondrial protein CLPB and leads to mitochondrial fragmentation, and structural changes in inhibited PRKCA trigger rapid relocalization to cell junctions. Thus, we identify the ATP-binding site as a major organizing center of kinase conformation and interaction. Our work suggests that these on-target, off-mechanism effects are likely to occur in other kinases as well, and provides the analytical framework to systematically characterize a frequently overlooked phenomenon highly relevant for understanding drug side effects to guide the development of novel therapeutics.

Protein Kinase Inhibitors↗

PubMed2026

Selective Enrichment of Newly Synthesized Proteins Using Phos-Tag Click Tip Enables Nascent Proteome Analysis in Influenza A Virus Infection.

Profiling of newly synthesized proteins (NSPs) provides access to dynamic changes in protein production that accompany acute cellular responses. Bioorthogonal noncanonical amino acid tagging (BONCAT)-based approaches enable selective labeling of NSPs; however, their broader application remains constrained by labor-intensive enrichment workflows and limited sensitivity for direct peptide-level analysis. Here, we developed a workflow termed "Phos-tag Click Tip" by integrating a phosphorylated variant of bicyclononyne (pBCN) with Phos-tag affinity purification to selectively capture azidohomoalanine (AHA)-labeled peptides for newly synthesized proteome analysis (NSProteomics). This approach overcomes key limitations of conventional proteomics and BONCAT-based strategies by enabling efficient enrichment and sensitive detection of NSP-derived peptides. Using this workflow, we performed comprehensive NSP profiling of host cells during influenza A virus infection. We identified dynamic changes in distinct NSP profiles associated with viral replication, host restriction, and immune responses, many of which were not readily detected with conventional whole-cell- or phospho-proteomic analyses. Overall, the Phos-tag Click Tip workflow provides a complementary approach for stimulus-responsive NSP profiling, offering functionally relevant insights into host-virus interactions and cellular response mechanisms.

Proteome↗

PubMed2026

Structural Characterization of Native RNA Polymerase II Transcription Complexes and Nucleosomes in Drosophila melanogaster.

Structural studies of eukaryotic RNA polymerase II (Pol II) transcription often rely on in vitro assembly, which may not fully represent native conditions. To investigate Pol II transcription in metazoan cells, we developed a method to isolate native transcription complexes from Drosophila melanogaster embryos using FLAG-tag affinity purification and Micrococcal Nuclease treatment. Cryo-EM and proteomics studies revealed diverse transcription complexes and nucleosomes, including a metazoan Rpb4/Rpb7 stalk-less Pol II elongation complex and a hexameric nucleosome lacking an H2A/H2B dimer. Notably, nucleosome is found only downstream of the nucleosome elongation complex, underscoring it as a major energy barrier and a time-consuming step during Pol II progression through chromatin. Proteomics identified co-purified factors involved in transcription initiation, elongation, and RNA modification. This study provides a framework for investigations of transcription in cells, paving the way for future studies of transient and minor complexes.

Animals↗

PubMed2026

The Lipid Interactome: an interactive and open access platform for exploring cellular lipid-protein interactions.

SUMMARY: Lipid-protein interactions play essential roles in cellular signaling and membrane dynamics, yet their systematic characterization has long been hindered by the inherent biochemical properties of lipids. Recent advances in functionalized lipid probes-equipped with photoactivatable crosslinkers, affinity handles, and photocleavable protecting groups-have enabled proteomics-based identification of lipid interacting proteins with unprecedented specificity and resolution. Despite the growing number of published lipid interactomes, there remains no centralized effort to harmonize, compare, or integrate these datasets. The Lipid Interactome addresses this gap by providing a structured, interactive web portal that adheres to FAIR data principles-ensuring that lipid interactome studies are Findable, Accessible, Interoperable, and Reusable. Through standardized data formatting, interactive visualizations, and direct cross-study comparisons, this resource enables researchers to systematically explore the protein-binding partners of diverse bioactive lipids. By consolidating and curating lipid interactome proteomics data from multiple studies, the Lipid Interactome database serves as a critical tool for deciphering the biological functions of lipids in cellularsystems. AVAILABILITY AND IMPLEMENTATION: This site can be viewed at LipidInteractome.org. All data are available for download. No user information is collected or necessary for data navigation, interaction, or download.

Proteins↗

PubMed2025

Bone Adhered Sediments as a Source of Target and Environmental DNA and Proteins.

In recent years, sediments from cave environments have provided invaluable insights into ancient hominids, as well as past fauna and flora. Unfortunately, however, sediments are not always collected during excavation. In this study, we analyzed an overlooked but abundant resource in archaeological collections - sediments adhered to bone. We performed metagenomics and metaproteomics analysis on sediment from several human skeletal elements, originating from Neolithic to Medieval sites in England. We were able to reconstruct a partial human genome, the genetic profile of which matches that recovered from the original skeletal element. Additionally, aDNA sequences matching the genomes of endogenous gut microbiome bacteria were identified. We also found the presence of genetic sequences corresponding to animals and plants. In particular, we managed to retrieve the partial genome and proteome of a Black Rat (Rattus rattus), sharing close genetic affinities to other medieval Rattus rattus. Our results demonstrate that material that is usually ignored or discarded, can be used to reveal information about the individual and the environmental conditions at the time of their death.

Animals↗

PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗

PubMed2027

Epitope Tagging and Coimmunoprecipitation to Identify Viral Protein Interactors.

Affinity purification-mass spectrometry (AP-MS) is a powerful proteomic approach for dissecting the interaction network between virus and host. Traditional AP-MS employs overexpression of viral proteins as baits to enrich host interactors. However, overexpressed viral proteins may mislocalize to inappropriate cellular compartments and trigger endoplasmic reticulum stress by overwhelming the protein-folding machinery, which leads to false identification of host factors. To overcome these limitations, we introduce an AP-MS strategy based on direct infection with an epitope-tagged chikungunya virus (CHIKV/myc-E2), which we used to successfully uncover two new antiviral factors in CHIKV cellular reservoirs-macrophages. In this protocol, we will describe this technique step by step: (1) design and construction of myc-tagged virus by advanced multi-fragment assembly, (2) in vitro transcription and preparation of infectious myc-tagged virus stocks, and (3) immunoprecipitation of myc-tagged viral protein and its interactome for mass spectrometry analysis. This strategy enables accurate identification of viral interactors in a physiologically relevant context, providing a framework for future proteomic studies using tagged viruses.

Chikungunya virus↗

PubMed2026

Enzyme kinetics shapes the growth response of metabolic networks.

Microbes adjust their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates. Average expression levels in large proteome sectors change coherently, while individual proteins show divergent shifts even within the same pathway. Here, we establish a metabolic model that integrates local enzyme kinetics and global network architecture to predict the joint growth response of proteins and metabolites. Under nutrient limitation, we predict a remarkably simple pattern of proteome reallocation with growth rate: protein expression levels change linearly but heterogeneously. For a given enzyme, the direction of change is determined by its local kinetic constants - catalytic rate and substrate affinity - and by the degree of nutrient restriction affecting its embedding pathway. This double-graded growth response of the proteome is mediated by restriction-dependent metabolite levels, which are predicted to decrease with growth rate in a nonlinear way. The model establishes three specific growth laws: protein expression changes of individual enzymes are negatively correlated with their expression and with their substrate saturation at high growth; average changes of pathways and larger functional sectors are correlated with their internal variance. These predictions are in quantitative agreement with measured system-wide proteomics and metabolomics data of E. coli. Enzyme-specific response patterns are a starting point for model-guided interventions into bacterial metabolism.

Kinetics↗

PubMed2026

Selenoprotein S associates with complexes governing membrane protein biogenesis and translation-associated processes.

Human selenoprotein S (selenos) is part of the integrated cellular stress response and linked to protein quality control and signaling pathways. Consequently, genetic polymorphisms of selenos are associated with increased risks for diabetes, dyslipidemia, and cardiovascular diseases. Determining the specific roles of selenos in these cellular pathways and diseases has been challenging, as selenos associates with a wide range of protein complexes. Thus, to map the cellular functions of selenos and uncover their interconnections, we used affinity purification and in vivo crosslinking to stabilize transient protein interactions, followed by proteomics to record the resulting selenos interactome. Through mapping of selenos protein partners, we found evidence that selenos associates with complexes responsible for the insertion of membrane proteins into the endoplasmic reticulum (ER) bilayer and their connected quality control components. Furthermore, selenos is also part of metabolic, trafficking, and mitochondrial pathways. Notably, proteins involved in translation preferentially associate with selenos when its C-terminal intrinsically disordered segment containing the redox-active motif is accessible. Together, these results identify the C-terminal redox loop of selenos as a central interaction hub connecting translation with ER membrane protein biogenesis and quality control.

Selenoproteins↗