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Journal articles on the topic 'Interactome networks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Vidal, M. "Interactome networks." European Journal of Cancer Supplements 6, no. 9 (2008): 7. http://dx.doi.org/10.1016/s1359-6349(08)71200-x.

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Vidal, Marc, Michael E. Cusick, and Albert-László Barabási. "Interactome Networks and Human Disease." Cell 144, no. 6 (2011): 986–98. http://dx.doi.org/10.1016/j.cell.2011.02.016.

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Cho, Young-Rae, and Aidong Zhang. "Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins." International Journal of Knowledge Discovery in Bioinformatics 1, no. 3 (2010): 20–35. http://dx.doi.org/10.4018/jkdb.2010070102.

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High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and pro

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Missiuro, Patrycja Vasilyev, Kesheng Liu, Lihua Zou, et al. "Information Flow Analysis of Interactome Networks." PLoS Computational Biology 5, no. 4 (2009): e1000350. http://dx.doi.org/10.1371/journal.pcbi.1000350.

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Aloy, Patrick. "Shaping the future of interactome networks." Genome Biology 8, no. 10 (2007): 316. http://dx.doi.org/10.1186/gb-2007-8-10-316.

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Scholtens, D., M. Vidal, and R. Gentleman. "Local modeling of global interactome networks." Bioinformatics 21, no. 17 (2005): 3548–57. http://dx.doi.org/10.1093/bioinformatics/bti567.

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Zitnik, Marinka, Rok Sosič, Marcus W. Feldman, and Jure Leskovec. "Evolution of resilience in protein interactomes across the tree of life." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4426–33. http://dx.doi.org/10.1073/pnas.1818013116.

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Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein–protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that inter

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Ur Rehman, Hafeez, Usman Habib, Umer Ijaz, Naveed Islam, Atta Ur Rehman Khan, and Raheel Nawaz. "Identification of Yeast’s Interactome Using Neural Networks." IEEE Access 7 (2019): 179634–45. http://dx.doi.org/10.1109/access.2019.2959401.

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Hannigan, Molly M., Alyson M. Hoffman, J. Will Thompson, Tianli Zheng, and Christopher V. Nicchitta. "Quantitative Proteomics Links the LRRC59 Interactome to mRNA Translation on the ER Membrane." Molecular & Cellular Proteomics 19, no. 11 (2020): 1826–49. http://dx.doi.org/10.1074/mcp.ra120.002228.

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Protein synthesis on the endoplasmic reticulum (ER) requires the dynamic coordination of numerous cellular components. Together, resident ER membrane proteins, cytoplasmic translation factors, and both integral membrane and cytosolic RNA-binding proteins operate in concert with membrane-associated ribosomes to facilitate ER-localized translation. Little is known, however, regarding the spatial organization of ER-localized translation. This question is of growing significance as it is now known that ER-bound ribosomes contribute to secretory, integral membrane, and cytosolic protein synthesis a

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Ni, Wen-yin, Hui-jun Xiong, Bi-hai Zhao, and Sai Hu. "Predicting overlapping protein complexes in weighted interactome networks." Journal of Zhejiang University SCIENCE C 14, no. 10 (2013): 756–65. http://dx.doi.org/10.1631/jzus.c13b0097.

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Kohli, Priyanka, Malte P. Bartram, Sandra Habbig, et al. "Label-free quantitative proteomic analysis of the YAP/TAZ interactome." American Journal of Physiology-Cell Physiology 306, no. 9 (2014): C805—C818. http://dx.doi.org/10.1152/ajpcell.00339.2013.

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The function of an individual protein is typically defined by protein-protein interactions orchestrating the formation of large complexes critical for a wide variety of biological processes. Over the last decade the analysis of purified protein complexes by mass spectrometry became a key technique to identify protein-protein interactions. We present a fast and straightforward approach for analyses of interacting proteins combining a Flp-in single-copy cellular integration system and single-step affinity purification with single-shot mass spectrometry analysis. We applied this protocol to the a

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Mehta, Virja, and Laura Trinkle-Mulcahy. "Recent advances in large-scale protein interactome mapping." F1000Research 5 (April 29, 2016): 782. http://dx.doi.org/10.12688/f1000research.7629.1.

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Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other ‘omics’ data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation o

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Rachlin, John, Dikla Dotan Cohen, Charles Cantor, and Simon Kasif. "Biological context networks: a mosaic view of the interactome." Molecular Systems Biology 2, no. 1 (2006): 66. http://dx.doi.org/10.1038/msb4100103.

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Yazaki, Junshi, Mary Galli, Alice Y. Kim, et al. "Mapping transcription factor interactome networks using HaloTag protein arrays." Proceedings of the National Academy of Sciences 113, no. 29 (2016): E4238—E4247. http://dx.doi.org/10.1073/pnas.1603229113.

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Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein–protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thous

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Janjić, Vuk, and Nataša Pržulj. "The Topology of the Growing Human Interactome Data." Journal of Integrative Bioinformatics 11, no. 2 (2014): 27–42. http://dx.doi.org/10.1515/jib-2014-238.

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Summary We have long moved past the one-gene-one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype-phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein-protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being be

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Serrao, Simone, Cristina Contini, Giulia Guadalupi, et al. "Salivary Cystatin D Interactome in Patients with Systemic Mastocytosis: An Exploratory Study." International Journal of Molecular Sciences 24, no. 19 (2023): 14613. http://dx.doi.org/10.3390/ijms241914613.

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Mastocytosis, a rare blood disorder characterized by the proliferation of clonal abnormal mast cells, has a variegated clinical spectrum and diagnosis is often difficult and delayed. Recently we proposed the cathepsin inhibitor cystatin D-R26 as a salivary candidate biomarker of systemic mastocytosis (SM). Its C26 variant is able to form multiprotein complexes (mPCs) and since protein–protein interactions (PPIs) are crucial for studying disease pathogenesis, potential markers, and therapeutic targets, we aimed to define the protein composition of the salivary cystatin D-C26 interactome associa

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Lappe, M., and L. Holm. "Algorithms for protein interaction networks." Biochemical Society Transactions 33, no. 3 (2005): 530–34. http://dx.doi.org/10.1042/bst0330530.

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The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs

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Kruse, Kevin, Jeff Klomp, Mitchell Sun, et al. "Analysis of biological networks in the endothelium with biomimetic microsystem platform." American Journal of Physiology-Lung Cellular and Molecular Physiology 317, no. 3 (2019): L392—L401. http://dx.doi.org/10.1152/ajplung.00392.2018.

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Here we describe a novel method for studying the protein “interactome” in primary human cells and apply this method to investigate the effect of posttranslational protein modifications (PTMs) on the protein’s functions. We created a novel “biomimetic microsystem platform” (Bio-MSP) to isolate the protein complexes in primary cells by covalently attaching purified His-tagged proteins to a solid microscale support. Using this Bio-MSP, we have analyzed the interactomes of unphosphorylated and phosphomimetic end-binding protein-3 (EB3) in endothelial cells. Pathway analysis of these interactomes d

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Lopez-Zaplana, Alvaro. "Deciphering Arabidopsis Aquaporin Networks: Comparative Analysis of the STRING and BioGRID Interactomes." International Journal of Plant Biology 16, no. 1 (2025): 28. https://doi.org/10.3390/ijpb16010028.

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Aquaporins are transmembrane proteins that mediate the transport of water, as well as various ions and molecules. In plants, they play a critical role in numerous processes, including stress adaptation, nutrition, cellular communication, and transpiration. Therefore, understanding the function and interactions of these proteins with others—known as interactomes—is of significant agronomic and biological interest. This study aims to analyse the interactome of all aquaporins in Arabidopsis thaliana L. using two distinct databases, STRING and BioGRID. After analysing both interactomes, a wide ran

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Maurya, Svetlana, Robert W. Mills, Konstantin Kahnert, et al. "Outlining cardiac ion channel protein interactors and their signature in the human electrocardiogram." Nature Cardiovascular Research 2, no. 7 (2023): 673–92. http://dx.doi.org/10.1038/s44161-023-00294-y.

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AbstractProtein–protein interactions are essential for normal cellular processes and signaling events. Defining these interaction networks is therefore crucial for understanding complex cellular functions and interpretation of disease-associated gene variants. We need to build a comprehensive picture of the interactions, their affinities and interdependencies in the specific organ to decipher hitherto poorly understood signaling mechanisms through ion channels. Here we report the experimental identification of the ensemble of protein interactors for 13 types of ion channels in murine cardiac t

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Larsen, Peter E., Frank Collart, and Yang Dai. "Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data." International Journal of Knowledge Discovery in Bioinformatics 1, no. 3 (2010): 1–19. http://dx.doi.org/10.4018/jkdb.2010070101.

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The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed metho

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Wang, Haojie, Meixia Ye, Yaru Fu, et al. "Modeling genome-wide by environment interactions through omnigenic interactome networks." Cell Reports 35, no. 6 (2021): 109114. http://dx.doi.org/10.1016/j.celrep.2021.109114.

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23

Wiles, Amy M., Mark Doderer, Jianhua Ruan, et al. "Building and analyzing protein interactome networks by cross-species comparisons." BMC Systems Biology 4, no. 1 (2010): 36. http://dx.doi.org/10.1186/1752-0509-4-36.

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Chin, Chia-Hao, Shu-Hwa Chen, Hsin-Hung Wu, Chin-Wen Ho, Ming-Tat Ko, and Chung-Yen Lin. "cytoHubba: identifying hub objects and sub-networks from complex interactome." BMC Systems Biology 8, Suppl 4 (2014): S11. http://dx.doi.org/10.1186/1752-0509-8-s4-s11.

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Tian, Ruijun, Haopeng Wang, Gerald D. Gish, et al. "Combinatorial proteomic analysis of intercellular signaling applied to the CD28 T-cell costimulatory receptor." Proceedings of the National Academy of Sciences 112, no. 13 (2015): E1594—E1603. http://dx.doi.org/10.1073/pnas.1503286112.

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Systematic characterization of intercellular signaling approximating the physiological conditions of stimulation that involve direct cell–cell contact is challenging. We describe a proteomic strategy to analyze physiological signaling mediated by the T-cell costimulatory receptor CD28. We identified signaling pathways activated by CD28 during direct cell–cell contact by global analysis of protein phosphorylation. To define immediate CD28 targets, we used phosphorylated forms of the CD28 cytoplasmic region to obtain the CD28 interactome. The interaction profiles of selected CD28-interacting pro

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Klein, Brennan, Erik Hoel, Anshuman Swain, Ross Griebenow, and Michael Levin. "Evolution and emergence: higher order information structure in protein interactomes across the tree of life." Integrative Biology 13, no. 12 (2021): 283–94. http://dx.doi.org/10.1093/intbio/zyab020.

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Abstract The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of h

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Fasano, Candida, Valentina Grossi, Giovanna Forte, and Cristiano Simone. "Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis." Cells 11, no. 23 (2022): 3739. http://dx.doi.org/10.3390/cells11233739.

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Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein–protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3–10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in

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Wierbowski, Shayne D., Tommy V. Vo, Pascal Falter-Braun, et al. "A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice." Proceedings of the National Academy of Sciences 117, no. 21 (2020): 11836–42. http://dx.doi.org/10.1073/pnas.1918068117.

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Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we developPCR-mediatedLinkage of barcodedAdaptersTo nucleic acidElements forsequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome forOryza sativacovering 2,300 genes and constructing a high-quality protein–protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for hi

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Andrade, Preema, Manjunatha Hanumanthappa, Nayana Prakash, and Vijayalakshmi Venkataramanaiah. "Construction and analysis of the interactome of eugenol to identify the modular proteins and their role in cancer." Research Journal of Biotechnology 19, no. 12 (2024): 143–50. https://doi.org/10.25303/1912rjbt1430150.

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Cancer is a disease resulting from abnormalities in complex biological networks. Rather than a single target, many potential drugs target multiple pathways through protein modulation. This leads to the motivation to develop methods that help to predict drug interactions in complex networks accurately. Eugenol, a phenylpropanoid, is used for the study. It has been found that eugenol is a drugable molecule that is effective in treating cancer. We have executed a computational platform that combines network pharmacology and systems biology to unravel the molecular mechanisms of the complex diseas

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Huttlin, Edward L., Raphael J. Bruckner, Joao A. Paulo, et al. "Architecture of the human interactome defines protein communities and disease networks." Nature 545, no. 7655 (2017): 505–9. http://dx.doi.org/10.1038/nature22366.

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Chen, Yong, Mingxin Gan, Rui Jiang, and Wangshu Zhang. "Constructing human phenome-interactome networks for the prioritization of candidate genes." Statistics and Its Interface 5, no. 1 (2012): 137–48. http://dx.doi.org/10.4310/sii.2012.v5.n1.a12.

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Anjo, Sandra I., Tania Martins-Marques, Paulo Pereira, Henrique Girão, and Bruno Manadas. "Elucidation of the dynamic nature of interactome networks: A practical tutorial." Journal of Proteomics 171 (January 2018): 116–26. http://dx.doi.org/10.1016/j.jprot.2017.04.011.

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Uzoma, Ijeoma, and Heng Zhu. "Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks." Genomics, Proteomics & Bioinformatics 11, no. 1 (2013): 18–28. http://dx.doi.org/10.1016/j.gpb.2012.12.005.

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Das, Jishnu, Robert Fragoza, Hao Ran Lee, et al. "Exploring mechanisms of human disease through structurally resolved protein interactome networks." Mol. BioSyst. 10, no. 1 (2014): 9–17. http://dx.doi.org/10.1039/c3mb70225a.

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Yazaki, Junshi, Mary Galli, Alice Y. Kim, and Joseph R. Ecker. "Profiling Interactome Networks with the HaloTag-NAPPA In Situ Protein Array." Current Protocols in Plant Biology 3, no. 3 (2018): e20071. http://dx.doi.org/10.1002/cppb.20071.

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Harms, Charlotte, James Warren, Hannah Voß, et al. "Pregnancy-Specific Glycoprotein 1 (PSG1) Interactome Networks in Health and Disease." Journal of Reproductive Immunology 159 (September 2023): 104091. http://dx.doi.org/10.1016/j.jri.2023.104091.

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Li, Shitao, Lingyan Wang, Michael Berman, and Martin Dorf. "Mapping a dynamic innate immunity protein interaction network regulating type I interferon production (108.2)." Journal of Immunology 188, no. 1_Supplement (2012): 108.2. http://dx.doi.org/10.4049/jimmunol.188.supp.108.2.

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Abstract We use co-immunoprecipitation and mass spectrometry to identify antiviral signaling networks which regulate innate immune responses. Fifty-eight baits were associated with 260 interacting proteins forming a Human Innate Immunity Interactome of 401 unique interactions; 21% of interactions were remodeled following RNA, DNA, or LPS stimulation. Overexpression and depletion analyses identified 22 novel genes which regulate NF-kB and ISRE reporter activity, viral replication, or virus-induced interferon production. The innate immune interactome provides a dynamic physical and regulatory ne

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Malod-Dognin, Noël, and Nataša Pržulj. "Functional geometry of protein interactomes." Bioinformatics 35, no. 19 (2019): 3727–34. http://dx.doi.org/10.1093/bioinformatics/btz146.

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Abstract Motivation Protein–protein interactions (PPIs) are usually modeled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactomes, including protein complexes. Results To model the multi-scale organization of these complex biological systems, we utilize simp

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Frohn, Anne, H. Christian Eberl, Julia Stöhr, et al. "Dicer-dependent and -independent Argonaute2 Protein Interaction Networks in Mammalian Cells." Molecular & Cellular Proteomics 11, no. 11 (2012): 1442–56. http://dx.doi.org/10.1074/mcp.m112.017756.

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Argonaute (Ago) proteins interact with small regulatory RNAs such as microRNAs (miRNAs) and facilitate gene-silencing processes. miRNAs guide Ago proteins to specific mRNAs leading to translational silencing or mRNA decay. In order to understand the mechanistic details of miRNA function, it is important to characterize Ago protein interactors. Although several proteomic studies have been performed, it is not clear how the Ago interactome changes on miRNA or mRNA binding. Here, we report the analysis of Ago protein interactions in miRNA-containing and miRNA-depleted cells. Using stable isotope

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Dohlman, Anders B., and Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference." Experimental Biology and Medicine 244, no. 6 (2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.

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Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behav

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Gipsi, Lima-Mendez, Faust Karoline, Henry Nicolas, et al. "Determinants of community structure in the global plankton interactome." Science 348, no. 6237 (2015): 833–940. https://doi.org/10.1126/science.1262073.

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Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns.We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated networkgenera

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Chen, Dijun, Liang-Yu Fu, Zhao Zhang, et al. "Dissecting the chromatin interactome of microRNA genes." Nucleic Acids Research 42, no. 5 (2013): 3028–43. http://dx.doi.org/10.1093/nar/gkt1294.

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Abstract Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We obs

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Song, Min Ok, Jianying Li, and Jonathan H. Freedman. "Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper." Physiological Genomics 38, no. 3 (2009): 386–401. http://dx.doi.org/10.1152/physiolgenomics.00083.2009.

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Copper is an essential trace element; however, at supraphysiological levels, it can be extremely toxic. Microarray data from HepG2 cells exposed to 100, 200, 400, and 600 μM copper for 4, 8, 12 and 24 h were generated and analyzed. Principal components, K-means, and hierarchical clustering, interactome, and pathway mapping analyses indicated that these exposure conditions induce physiological and toxicological changes in the HepG2 transcriptome. As a general trend, when the level of toxicity increases, the number and diversity of affected genes, Gene Ontology categories, regulatory pathways, a

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Huttlin, Edward L., Raphael J. Bruckner, Jose Navarrete-Perea, et al. "Dual proteome-scale networks reveal cell-specific remodeling of the human interactome." Cell 184, no. 11 (2021): 3022–40. http://dx.doi.org/10.1016/j.cell.2021.04.011.

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Vidal, Marc. "Interactome Networks." FASEB Journal 22, S1 (2008). http://dx.doi.org/10.1096/fasebj.22.1_supplement.262.1.

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Gogl, Gergo, Boglarka Zambo, Camille Kostmann, et al. "Quantitative fragmentomics allow affinity mapping of interactomes." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-33018-0.

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AbstractHuman protein networks have been widely explored but most binding affinities remain unknown, hindering quantitative interactome-function studies. Yet interactomes rely on minimal interacting fragments displaying quantifiable affinities. Here, we measure the affinities of 65,000 interactions involving PDZ domains and their target PDZ-binding motifs (PBM) within a human interactome region particularly relevant for viral infection and cancer. We calculate interactomic distances, identify hot spots for viral interference, generate binding profiles and specificity logos, and explain selecte

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Leib, Lisa, Jana Juli, Liane Jurida та ін. "The proximity-based protein interactome and regulatory logics of the transcription factor p65 NF-κB/RELA". EMBO Reports, 3 січня 2025. https://doi.org/10.1038/s44319-024-00339-8.

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AbstractThe protein interactome of p65/RELA, the most active subunit of the transcription factor (TF) NF-κB, has not been previously determined in living cells. Using p65-miniTurbo fusion proteins and biotin tagging, we identify >350 RELA interactors from untreated and IL-1α-stimulated cells, including many TFs (47% of all interactors) and >50 epigenetic regulators belonging to different classes of chromatin remodeling complexes. A comparison with the interactomes of two point mutants of p65 reveals that the interactions primarily require intact dimerization rather than DNA-binding prope

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Calderone, Alberto, Luisa Castagnoli, and Gianni Cesareni. "mentha: a resource for browsing integrated protein-interaction networks." July 30, 2013. https://doi.org/10.1038/nmeth.2561.

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About mentha mentha archives evidence collected from different sources and presents these data in a complete and comprehensive way. Its data comes from manually curated protein-protein interaction databases that have adhered to the IMEx consortium. The aggregated data forms an interactome which includes many organisms. mentha is a resource that offers a series of tools to analyse selected proteins in the context of a network of interactions. Protein interaction databases archive protein-protein interaction (PPI) information from published articles. However, no database alone has sufficient lit

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Maddamsetti, Rohan. "Selection Maintains Protein Interactome Resilience in the Long-Term Evolution Experiment with Escherichia coli." Genome Biology and Evolution 13, no. 6 (2021). http://dx.doi.org/10.1093/gbe/evab074.

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Abstract Most cellular functions are carried out by a dynamic network of interacting proteins. An open question is whether the network properties of protein interactomes represent phenotypes under natural selection. One proposal is that protein interactomes have evolved to be resilient, such that they tend to maintain connectivity when proteins are removed from the network. This hypothesis predicts that interactome resilience should be maintained by natural selection during long-term experimental evolution. I tested this prediction by modeling the evolution of protein–protein interaction (PPI)

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Joshi, Suhasini, Erica DaGama Gomes, Tai Wang, et al. "Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer." Communications Biology 4, no. 1 (2021). http://dx.doi.org/10.1038/s42003-021-02842-3.

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AbstractCancer cell plasticity due to the dynamic architecture of interactome networks provides a vexing outlet for therapy evasion. Here, through chemical biology approaches for systems level exploration of protein connectivity changes applied to pancreatic cancer cell lines, patient biospecimens, and cell- and patient-derived xenografts in mice, we demonstrate interactomes can be re-engineered for vulnerability. By manipulating epichaperomes pharmacologically, we control and anticipate how thousands of proteins interact in real-time within tumours. Further, we can essentially force tumours i

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