A reference map of the human binary protein interactome

Luck, Katja; Kim, Dae-Kyum; Lambourne, L.; Spirohn, Kerstin; Begg, Bridget E.; Bian, Wenting; Brignall, Ruth; Cafarelli, Tiziana M.; Campos‐Laborie, Francisco J.; Charloteaux, Benoît; Choi, Dong-Sic; Coté, Atina G.; Daley, Meaghan; Deimling, Steven; Desbuleux, Alice; Dricot, Amélie; Gebbia, Marinella; Hardy, Madeleine F.; Kishore, Nishka; Knapp, Jennifer J.; Kovács, I.; Lemmens, Irma; Mee, Miles W; Mellor, Joseph; Pollis, Carl; Pons, Carles; Richardson, Aaron; Schlabach, Sadie; Teeking, Bridget; Yadav, A. S.; Babor, Mariana; Balcha, Dawit; Basha, Omer; Bowman-Colin, Christian; Chin, Suet Feung; Choi, Soon Gang; Colabella, Claudia; Coppin, Georges; D’Amata, Cassandra; Ridder, David De; Rouck, Steffi De; Duran‐Frigola, Miquel; Ennajdaoui, Hanane; Goebels, Florian; Goehring, Liana; Gopal, Anand; Haddad, Ghazal; Hatchi, Elodie; Helmy, Mohamed; Jacob, Yves; Kassa, Yoseph; Landini, Serena; Li, Roujia; Lieshout, Natascha van; MacWilliams, Andrew; Markey, Dylan; Paulson, Joseph N.; Rangarajan, Sudharshan; Rasla, John; Rayhan, Ashyad; Rolland, Thomas; San-Miguel, Adriana; Shen, Yun; Sheykhkarimli, Dayag; Sheynkman, Gloria; Simonovsky, Eyal; Taşan, Murat; Tejeda, Alexander O.; Tropepe, Vincent; Twizere, Jean-Claude; Wang, Yang; Weatheritt, Robert J.; Weile, Jochen; Xia, Yu; Yang, Xinping; Yeger-Lotem, Esti; Zhong, Quan; Aloy, Patrick; Bader, Gary D.; Rivas, Javier De Las; Gaudet, Suzanne; Hao, Tong; Rak, Janusz; Tavernier, Jan; Hill, David E.; Vidal, Marc; Roth, Frederick P.; Calderwood, Michael A.

doi:10.1038/s41586-020-2188-x

Cited by 930 publications

(966 citation statements)

References 53 publications

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“…We verified whether any of the 2,000 novel PPIs came up in recent interactome maps HuRI (HI-Union) 14 and BioPlex 15 . While there was no overlap with HuRI union dataset, there were 8 PPIs in the BioPlex map (ADAM9-ADAM32, P3H3-OS9, PVR-NECTIN2, SRRM2-SNIP1, PABPC4-LUC7L2, PRKACA-AKAP1, NDUFA13-ECSIT, and NPTX1-NPTX2).…”

Section: Resultsmentioning

confidence: 87%

“…The small overlap is not surprising because even high-throughput biotechnological methods discover different parts of the interactome with only small overlaps with each other6, this demonstrating complementary strengths. 14 Applications of this network are two-fold: (1) biologists, who typically focus their research on specific proteins or a pathway may look up the novel interactions relevant to that protein or pathway (e.g. 26 ) (2) computational systems biologists may investigate it in conjunction with transcriptomic/proteomic data (e.g.).…”

Section: Resultsmentioning

confidence: 99%

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Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Karunakaran

Ganapathiraju

2020

Preprint

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World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as “PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'”.

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Karunakaran

Ganapathiraju

2020

Preprint

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“…Constructing the Human Protein-Protein Interactome -We assembled 15 commonly used databases, focusing on high-quality PPIs with five types of evidences: (1) binary, physical PPIs tested by highthroughput yeast-two-hybrid (Y2H) screening system, combining binary PPIs tested from three publicly available high-quality Y2H datasets [56][57][58], (2) analysed as a control to remove all non-specific interactions (Fig.3B).…”

Section: Lc-ms/ms Analysis Of Clarified Total Lysates (Inputs) Was Pementioning

confidence: 99%

Proteomic Analysis Uncovers Measles Virus Protein C Interaction with p65/iASPP/p53 Protein Complex

Meignié

Combredet

Santolini

et al. 2020

Preprint

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Viruses manipulate central machineries of host cells to their advantage. They prevent host cell antiviral responses to create a favorable environment for their survival and propagation. Measles virus (MV) encodes two non-structural proteins MV-V and MV-C known to counteract the host interferon response and to regulate cell death pathways. Several molecular mechanisms underlining MV-V regulation of innate immunity and cell death pathways have been proposed, whereas MV-C host protein partners are less studied. We suggest that some cellular factors that are controlled by MV-C protein during viral replication could be components of innate immunity and the cell death pathways. To determine which host factors are targeted by MV-C, we captured both direct and indirect host protein partners of MV-C protein. For this, we used a strategy based on recombinant viruses expressing tagged viral proteins followed by affinity purification and a bottom-up mass spectrometry analysis. From the list of host proteins specifically interacting with MV-C protein in different cell lines we selected the host targets that belong to immunity and cell death pathways for further validation. Direct protein partners of MV-C were determined by applying protein complementation assay (PCA) and the bioluminescence resonance energy transfer (BRET) approach. As a result, we found that MV-C protein specifically interacts with p65/iASPP/p53 protein complex that controls both cell death and innate immunity pathways.

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“…Estimates of the total number of human PPIs range from 130,000 to 600,000 (Bork et al, 2004;Stumpf et al, 2008;Venkatesan et al, 2009), to 3,000,000 (Luck et al, 2020), several orders of magnitude larger than the D melanogaster interactome. High-throughput experimental and theoretical approaches are being used to build PPI networks.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, in year 2012, there were more than 235,000 binary interactions reported (Licata et al, 2012). Most protein interaction databases (DB) -bioGRID (Chatr-aryamontri et al, 2017), BIND (Alfarano et al, 2005), STRING (Szklarczyk et al, 2017), IntAct (Orchard et al, 2014), MINT (Licata et al, 2012), MENTHA (Calderone et al, 2013), Clinically relevant GPCR PPI mapping (Sokolina et al, 2017), HPRD (Peri et al, 2003;Keshava Prasad et al, 2009), HINT (Das and Yu, 2012), CORUM (Ruepp et al, 2010;Giurgiu et al, 2019), APID (Alonso-López et al, 2019, HI-II-14 (Hwang et al, 2016), HI-III-20 (Luck et al, 2020), BioPlex (Huttlin et al, 2015), QUBIC (Huang, 2015), IID (Kotlyar et al, 2019) -offer general information about experimentally validated PPIs of all types. The IMEx Consortium groups all nonredundant protein interaction data in one interface (Orchard et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Khazen

Gyulkhandanian

Issa

et al. 2019

Preprint

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Membrane proteins account for about one-third of the proteomes of organisms and include structural proteins, channels, and receptors. The mutual interactions they establish in the membrane play crucial roles in organisms, as they are behind many processes such as cell signaling and protein function. Because of their number and diversity, these proteins and their macromolecular complexes, along with their physical interfaces represent potential pharmacological targets par excellence for a variety of diseases, with very important implications for the design and discovery of new drugs or peptides modulating or inhibiting the interaction. Yet, structural data coming from experiments is very scarce for this family of proteins. To overcome this problem, we propose a computational approach for the prediction of alpha transmembrane protein multimeric higher-order structures through data mining, sequence analysis, motif search, extraction, identification and characterization of the amino acid residues at the interface of the complexes. This method leads us to the formulation of binding sites used to scan protein sequence datasets for generating new potential interacting protein couples. Our template motif-based approach using experimental recognition sites leads us to predict new binding sites and to thousands of new binary complexes between membrane proteins when allowing amino acid mutations to take place. We generate an online database of the annotated predicted interactions.

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A reference map of the human binary protein interactome

Cited by 930 publications

References 53 publications

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Proteomic Analysis Uncovers Measles Virus Protein C Interaction with p65/iASPP/p53 Protein Complex

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

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