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Results for “Protein Interaction Maps”

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At least 19 recordsLinked to original sources

PubMed2026

SARS-CoV-2 Orf3a protein interaction mapping using unnatural amino acid incorporation.

Mapping transient protein-protein interactions remain a major challenge in studying viral host-pathogen interfaces. While some virus-host interactions are stable and readily captured, the majority are highly dynamic, reflecting the need for viral proteins to engage distinct host factors at different stages of the life cycle. Here, we employ a protein engineering strategy based on the site-specific incorporation of the unnatural acid p-azido-L-phenylalanine (AzF) to enable photo-crosslinking proteomic analysis of the SARS-CoV-2 accessory protein Orf3a in live cells. Genetic installation of AzF at residue K198 of Orf3a permitted UV-induced covalent capture of proximal host interacting proteins, overcoming challenges associated with membrane localization and limited protein abundance. A total of 248 high-confidence Orf3a-interacting proteins were reproducibly identified and subjected to gene ontology analysis, revealing enrichment in innate immune signaling, antiviral defense, RNA processing, and viral replication-associated pathways. Orf3a is an accessory protein that functions as a viroporin and traffics across multiple cellular compartments, and was found to interact with host RNA helicases, RNA-binding proteins, immune regulators, and metabolic enzymes implicated in SARS-CoV-2 infection. Together, these results demonstrate that genetically encoded, site-specific photo-crosslinking enables selective capture of transient interactions that are often missed by nonspecific 254 nm UV crosslinking approaches and highlights Orf3a as a multifunctional protein that engages diverse host pathways. More broadly, this study establishes a generalizable framework for leveraging unnatural amino acid-based protein engineering approaches to interrogate dynamic host-pathogen interactions.

Humans↗

PubMed2026

Library-based, multiplexed strategy for mapping protein interaction networks via crosslinking.

BACKGROUND: Protein-protein interactions are fundamental to cellular function, yet resolving their interaction interfaces and dynamic behaviors in native biological contexts remains challenging, particularly for weak or transient interactions. Crosslinking strategies based on noncanonical amino acids offer an effective means to capture such interactions; however, traditional single-site incorporation provides limited coverage and may overlook critical interaction hotspots. RESULTS: By employing a mutagenesis library, multiple interaction partners and cross-linking sites of a target protein can be simultaneously screened in a single experiment, without prior knowledge of its precise structural or functional features, enabling effective and unbiased analysis of its interaction network. In this study, we constructed an amber codon-scanning mutagenesis library of PSMD10, facilitating independent incorporation of the photocrosslinking ncAA p-azido-phenylalanine at multiple distinct residues. This approach allowed us to systematically interrogate and precisely map potential interaction regions across the protein surface. Coupled with crosslinking mass spectrometry, we identified multiple residues involved in intermolecular interactions, as well as previously unreported interaction partners, including T2FA, TBA1C, and ATRIP. CONCLUSIONS: These findings expand our understanding of PSMD10-associated proteasome interactome, demonstrate a multiplexed strategy for in situ mapping of protein interaction interfaces with broad coverage, and offer a valuable platform for developing therapeutics that target protein-protein interactions.

Protein Interaction Mapping↗

PubMed2026

Quantitative interactome mapping of skeletal muscle insulin resistance.

Protein-protein interactions (PPIs) are dynamic and critical to adaptive homeostasis. While there have been massive efforts to catalogue proteome-wide PPIs, global quantification of changes remains a challenge. Here, we integrate dynamic protein correlation profiling - mass spectrometry (PCP-MS) and quantitative cross linking-mass spectrometry (qXL-MS) using multiplexed stable isotope labelling to characterise global PPI remodelling following the development of chronic skeletal muscle insulin resistance (IR) with or without acute insulin stimulation. We quantify >7,000 unique PPIs amongst 5,346 proteins and show changes in the interactome network dominate the proteome response. Our data show the dysregulation of protein processing in the endoplasmic/sarcoplasmic reticulum involving changes in PPIs with protein chaperones and disulfide isomerases is a major hallmark of skeletal muscle IR. Mechanistically, we show the dysregulation of PPIs with Protein-Disulfide Isomerase 6 (PDIA6) regulates cysteine oxidation and insulin sensitivity. Taken together, we show in vivo quantitative interactome mapping is a powerful approach to understand disease mechanisms and provide new insights into protein network re-organisations with IR.

Insulin Resistance↗

PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗

PubMed2025

Identification of Plant Chromatin Interaction Networks Using IP-MS and co-IP.

Proteins often act in concert to perform their function. Thus, the identification of protein complexes is crucial if we want to understand how they work. In this chapter, we present a highly sensitive protocol for the immunoprecipitation of nuclear chromatin-linked proteins in Arabidopsis thaliana that does not rely on time-consuming nuclei extraction. Interaction partners are identified using mass spectrometry and confirmed by co-immunoprecipitation. To help solubilize chromatin-bound proteins and eliminate nonspecific interactions of proteins binding the same DNA stretch, we include an enzymatic digestion step to remove DNA before immunoprecipitation. Our protocol offers a simplified process using optimized buffers, which facilitates quick and effective immunoprecipitation. The outcome is high-quality eluates that are ideal for identifying proteins through MS.

Chromatin↗

PubMed2026

ChromID: A Protocol for Mapping Protein Chromatin Interactions in Living Cells.

Chromatin modifications regulate genome function by recruiting proteins that control transcription, genome organization, and DNA repair. Identifying the proteins associated with specific chromatin modifications is therefore essential for understanding how these regulatory processes operate. Traditional approaches, including chromatin immunoprecipitation and affinity purification coupled to mass spectrometry, have uncovered many chromatin-associated proteins. However, they often rely on crosslinking and chromatin fragmentation, which can disrupt native chromatin architecture and limit the detection of transient interactions. Here, we describe a proximity-labeling protocol for identifying the chromatin-dependent protein interactome associated with specific chromatin marks, termed ChromID. ChromID uses engineered chromatin readers (eCRs) fused to a promiscuous biotin ligase, which labels proteins in the immediate vicinity of the targeted chromatin mark. The protocol includes in vivo biotin labeling, nuclear extract preparation, streptavidin-based enrichment, and tryptic digestion for downstream LC-MS/MS analysis. The protocol has been validated across multiple cell types and chromatin contexts and can be extended to other chromatin-associated proteins, providing a versatile approach to profile chromatin-associated proteomes within their native cellular environment. Key features • Maps proteins associated with different chromatin modifications in living cells using engineered chromatin readers fused to TurboID, BASU, or other promiscuous biotin ligases. • Preserves native chromatin organization and captures transient chromatin-associated interactions that are often lost during conventional affinity purification workflows. • Validated across multiple chromatin contexts, including histone modifications, DNA methylation, transcription factors, RNA polymerase II, and DNA damage-associated chromatin states. • Applicable to diverse cell types and organisms and adaptable to other chromatin-associated proteins, including transcription factors and chromatin regulators.

Biotin proximity labeling↗

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

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗

PubMed2005

Molecular determinants for PspA-mediated repression of the AAA transcriptional activator PspF.

The Escherichia coli phage shock protein system (pspABCDE operon and pspG gene) is induced by numerous stresses related to the membrane integrity state. Transcription of the psp genes requires the RNA polymerase containing the sigma(54) subunit and the AAA transcriptional activator PspF. PspF belongs to an atypical class of sigma(54) AAA activators in that it lacks an N-terminal regulatory domain and is instead negatively regulated by another regulatory protein, PspA. PspA therefore represses its own expression. The PspA protein is distributed between the cytoplasm and the inner membrane fraction. In addition to its transcriptional inhibitory role, PspA assists maintenance of the proton motive force and protein export. Several lines of in vitro evidence indicate that PspA-PspF interactions inhibit the ATPase activity of PspF, resulting in the inhibition of PspF-dependent gene expression. In this study, we characterize sequences within PspA and PspF crucial for the negative effect of PspA upon PspF. Using a protein fragmentation approach, we show that the integrity of the three putative N-terminal alpha-helical domains of PspA is crucial for the role of PspA as a negative regulator of PspF. A bacterial two-hybrid system allowed us to provide clear evidence for an interaction in E. coli between PspA and PspF in vivo, which strongly suggests that PspA-directed inhibition of PspF occurs via an inhibitory complex. Finally, we identify a single PspF residue that is a binding determinant for PspA.

Bacterial Proteins↗

PubMed2005

Oct-1 counteracts autoinhibition of Runx2 DNA binding to form a novel Runx2/Oct-1 complex on the promoter of the mammary gland-specific gene beta-casein.

The transcription factor Runx2 is essential for the expression of a number of bone-specific genes and is primarily considered a master regulator of bone development. Runx2 is also expressed in mammary epithelial cells, but its role in the mammary gland has not been established. Here we show that Runx2 forms a novel complex with the ubiquitous transcription factor Oct-1 to regulate the expression of the mammary gland-specific gene beta-casein. The Runx2/Oct-1 complex forms on a Runx/octamer element which is highly conserved in casein promoters. Chromatin immunoprecipitation, RNA interference, promoter mutagenesis, and transient expression analyses were used to demonstrate that the Runx2/Oct-1 complex contributes to the transcriptional regulation of the beta-casein gene. Analysis of the complex revealed autoinhibitory domains for DNA binding in both the N-terminal and the C-terminal regions of Runx2. Oct-1 stimulates the recruitment of Runx2 to the beta-casein promoter by interacting with the C-terminal region of Runx2, suggesting that Oct-1 stimulates Runx2 recruitment by relieving the autoinhibition of Runx2 DNA binding. These findings demonstrate that Runx2 collaborates with Oct-1 and contributes to the expression of a mammary gland-specific gene.

Animals↗

PubMed2005

Interaction preferences across protein-protein interfaces of obligatory and non-obligatory components are different.

BACKGROUND: A polypeptide chain of a protein-protein complex is said to be obligatory if it is bound to another chain throughout its functional lifetime. Such a chain might not adopt the native fold in the unbound form. A non-obligatory polypeptide chain associates with another chain and dissociates upon molecular stimulus. Although conformational changes at the interaction interface are expected, the overall 3-D structure of the non-obligatory chain is unaltered. The present study focuses on protein-protein complexes to understand further the differences between obligatory and non-obligatory interfaces. RESULTS: A non-obligatory chain in a complex of known 3-D structure is recognized by its stable existence with same fold in the bound and unbound forms. On the contrary, an obligatory chain is detected by its existence only in the bound form with no evidence for the native-like fold of the chain in the unbound form. Various interfacial properties of a large number of complexes of known 3-D structures thus classified are comparatively analyzed with an aim to identify structural descriptors that distinguish these two types of interfaces. We report that the interaction patterns across the interfaces of obligatory and non-obligatory components are different and contacts made by obligatory chains are predominantly non-polar. The obligatory chains have a higher number of contacts per interface (20 +/- 14 contacts per interface) than non-obligatory chains (13 +/- 6 contacts per interface). The involvement of main chain atoms is higher in the case of obligatory chains (16.9 %) compared to non-obligatory chains (11.2 %). The beta-sheet formation across the subunits is observed only among obligatory protein chains in the dataset. Apart from these, other features like residue preferences and interface area produce marginal differences and they may be considered collectively while distinguishing the two types of interfaces. CONCLUSION: These results can be useful in distinguishing the two types of interfaces observed in structures determined in large-scale in the structural genomics initiatives, especially for those multi-component protein assemblies for which the biochemical characterization is incomplete.

Animals↗

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

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗

PubMed2025

Tumor Signatures of Physical Fitness: Insights from a Preclinical Model.

PURPOSE: Cardiorespiratory fitness (CRF) and muscle strength are associated with cancer risk/mortality in adults. However, there is yet no evidence for pediatric tumors. This study investigated the association of CRF and muscle strength with several tumor-related phenotypes in an aggressive childhood malignancy, high-risk neuroblastoma. METHODS: Twelve mice-bearing orthotopic high-risk neuroblastomas were studied. CRF and muscle strength were assessed using treadmill and grip strength testing, respectively. The following tumor-related outcomes were studied: survival, clinical severity, tumor weight/volume, metastasis, and intratumor immune infiltrates. In addition, tumor samples underwent quantitative proteomic analysis via liquid chromatography-tandem mass spectrometry. Spearman correlations (or logistic regression) were performed between CRF/muscle strength and the abovementioned variables. Proteins that were significantly correlated with CRF or muscle strength were mapped into protein-protein interaction (PPI) networks using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. RESULTS: CRF was inversely correlated with clinical severity score ( r = -0.657, P = 0.020). Of 6840 identified tumor proteins, 76 correlated significantly with CRF (19 positively, 57 negatively), whereas 194 correlated with muscle strength (97 positively, 97 negatively). Proteins correlated with CRF were primarily involved in metabolic and structural pathways, including angiotensinogen and elastin. In turn, muscle strength-associated proteins were more abundant and included keratin family proteins (e.g., keratin, type I cytoskeletal 14, and type II cytoskeletal 5), proteins involved in cell adhesion (e.g., desmoglein-1-alpha), and translational regulators (e.g., eukaryotic initiation factor 4A). Network analysis revealed significant enrichment in structural organization and cellular adhesion pathways. CONCLUSIONS: Besides the association of CRF with clinical severity of the tumor, distinct novel tumor proteomic signatures associated with CRF and muscle strength were identified, highlighting potential mechanisms linking physical fitness with childhood cancer biology.

Muscle Strength↗

PubMed2026

Comprehensive Analysis of miRNAs and Predicted Protein Interaction Networks in Skeletal Muscle Development of Myostatin-Deficient Rabbits.

Myostatin (MSTN), encoded by the MSTN gene, is a critical negative regulator of skeletal muscle mass. This study aims to identify and characterize the miRNAs involved in the development of the double-muscling phenotype in MSTN-deficient rabbits. We performed high-throughput sequencing to analyze the miRNA expression profiles in gluteus maximus tissue from wild type (MSTN+/+) and MSTN-KO (MSTN+/- and MSTN-/- inclusive) rabbits. Differentially expressed miRNAs (DEmiRNAs) were identified, and their potential target genes were predicted. Functional enrichment analysis of these target mRNAs was conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to elucidate the involved biological pathways and regulatory networks. A total of 25 DEmiRNAs (13 downregulated and 12 upregulated, |log2FC| ≥ 1.0, adjusted p < 0.05) and 1178 differentially expressed mRNAs (408 upregulated and 770 downregulated, |log2FC| ≥ 2.0, adjusted p < 0.05) were identified in MSTN-KO compared to MSTN+/+ rabbits. Bioinformatics analysis revealed that the target genes of these DEmiRNAs were significantly enriched in key pathways governing muscle growth and metabolism, including the PI3K-Akt signaling pathway, MAPK signaling pathway, and pathways related to ECM-receptor interaction and insulin signaling. Notably, many predicted target mRNAs are expressed by genes that encode key inhibitors of myogenesis (e.g., HDAC4) and major extracellular matrix components (e.g., COL4A3, POSTN). Our results demonstrate that MSTN deficiency induces a distinct and widespread change in the miRNA expression landscape of skeletal muscle.

Animals↗

PubMed2026

Network pharmacology insights into the mechanistic basis of Taohe Chengqi Decoction in the treatment of constipation.

Constipation is a common gastrointestinal disorder associated with impaired motility, inflammation, and altered neuro-intestinal regulation. Taohe Chengqi Decoction, a classical prescription from Shang Han Lun, has been widely applied in the treatment of constipation, yet its pharmacological mechanisms remain insufficiently understood. We integrated systems pharmacology and network analysis to elucidate the therapeutic mechanisms of Taohe Chengqi Decoction against constipation. Active compounds and their putative targets were retrieved from traditional Chinese medicine systems pharmacology and PubChem, while constipation-related genes were collected from GeneCards and OMIM. Shared targets were identified and subsequently analyzed using STRING to construct a protein-protein interaction network. Hub proteins were ranked by degree centrality. A drug-disease-target network was built to map the interactions between Taohe Chengqi Decoction and constipation. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed to uncover functional modules and signaling pathways. A total of 188 common targets were identified. Protein-protein interaction network analysis highlighted AKT1, interleukin-6 (IL6), IL1B, and JUN as hub proteins, suggesting central roles in regulating inflammation, apoptosis, and signal transduction. Additional nodes with high connectivity, such as caspase-3, PTGS2, signal transducer and activator of transcription 3, hypoxia-inducible factor-1α, estrogen receptor 1, and epidermal growth factor receptor, were implicated in apoptosis, oxidative stress, and transcriptional regulation. The drug-disease-target network revealed a dense and highly interconnected structure, reflecting the multicomponent, multi-target nature of Taohe Chengqi Decoction. Kyoto encyclopedia of genes and genomes enrichment indicated significant involvement of the advanced glycation end-product binding to their receptor signaling pathway, along with IL-17, TNF, and HIF-1 pathways, underscoring the contribution of inflammatory and oxidative stress-related processes. This study, based on computational pharmacology analysis, predicts that Taohe Chengqi Decoction may exert therapeutic effects on constipation through an integrated regulation involving multiple components, targets, and pathways. The potential mechanisms are likely associated with the modulation of inflammatory responses, apoptosis, and oxidative stress, with the advanced glycation end-product binding to their receptor signaling pathway possibly acting as a key mediator. These findings provide theoretical insights and future directions for elucidating the molecular mechanisms underlying the therapeutic effects of Taohe Chengqi Decoction against constipation.

Drugs, Chinese Herbal↗

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↗

PubMed2024

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Proteomics↗