Maximizing binary interactome mapping with a minimal number of assays

Soon Gang Choi, Julien Olivet, Patricia Cassonnet, Pierre-Olivier Vidalain, Katja Luck, Luke Lambourne, Kerstin Spirohn, Irma Lemmens, Mélanie Dos Santos, Caroline Demeret, Louis Jones, Sudharshan Rangarajan, Wenting Bian, Eloi P. Coutant, Yves L. Janin, Sylvie van der Werf, Philipp Trepte, Erich E. Wanker, Javier De Las Rivas, Jan Tavernier, Jean-Claude Twizere, Tong Hao, David E. Hill, Marc Vidal (), Michael A. Calderwood () and Yves Jacob ()
Additional contact information
Soon Gang Choi: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Julien Olivet: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Patricia Cassonnet: Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité
Pierre-Olivier Vidalain: Équipe Chimie, Biologie, Modélisation et Immunologie pour la Thérapie (CBMIT), Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques (LCBPT), Centre Interdisciplinaire Chimie Biologie-Paris (CICB-Paris), UMR8601, CNRS, Université Paris Descartes
Katja Luck: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Luke Lambourne: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Kerstin Spirohn: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Irma Lemmens: Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB)
Mélanie Dos Santos: Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité
Caroline Demeret: Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité
Louis Jones: Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur
Sudharshan Rangarajan: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Wenting Bian: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Eloi P. Coutant: Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS
Yves L. Janin: Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS
Sylvie van der Werf: Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité
Philipp Trepte: Neuroproteomics, Max Delbrück Center for Molecular Medicine
Erich E. Wanker: Neuroproteomics, Max Delbrück Center for Molecular Medicine
Javier De Las Rivas: University of Salamanca (USAL), Campus Miguel de Unamuno
Jan Tavernier: Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB)
Jean-Claude Twizere: University of Liège
Tong Hao: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
David E. Hill: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Marc Vidal: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Michael A. Calderwood: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)
Yves Jacob: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI)

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.

Date: 2019
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DOI: 10.1038/s41467-019-11809-2

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