Literature DB >> 20946815

High-quality binary interactome mapping.

Matija Dreze1, Dario Monachello, Claire Lurin, Michael E Cusick, David E Hill, Marc Vidal, Pascal Braun.   

Abstract

Physical interactions mediated by proteins are critical for most cellular functions and altogether form a complex macromolecular "interactome" network. Systematic mapping of protein-protein, protein-DNA, protein-RNA, and protein-metabolite interactions at the scale of the whole proteome can advance understanding of interactome networks with applications ranging from single protein functional characterization to discoveries on local and global systems properties. Since the early efforts at mapping protein-protein interactome networks a decade ago, the field has progressed rapidly giving rise to a growing number of interactome maps produced using high-throughput implementations of either binary protein-protein interaction assays or co-complex protein association methods. Although high-throughput methods are often thought to necessarily produce lower quality information than low-throughput experiments, we have recently demonstrated that proteome-scale interactome datasets can be produced with equal or superior quality than that observed in literature-curated datasets derived from large numbers of small-scale experiments. In addition to performing all experimental steps thoroughly and including all necessary controls and quality standards, careful verification of all interacting pairs and validation tests using independent, orthogonal assays are crucial to ensure the release of interactome maps of the highest possible quality. This chapter describes a high-quality, high-throughput binary protein-protein interactome mapping pipeline that includes these features.
Copyright © 2010 Elsevier Inc. All rights reserved.

Mesh:

Year:  2010        PMID: 20946815     DOI: 10.1016/S0076-6879(10)70012-4

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  68 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2015-08-31       Impact factor: 11.205

2.  Mapping transcription factor interactome networks using HaloTag protein arrays.

Authors:  Junshi Yazaki; Mary Galli; Alice Y Kim; Kazumasa Nito; Fernando Aleman; Katherine N Chang; Anne-Ruxandra Carvunis; Rosa Quan; Hien Nguyen; Liang Song; José M Alvarez; Shao-Shan Carol Huang; Huaming Chen; Niroshan Ramachandran; Stefan Altmann; Rodrigo A Gutiérrez; David E Hill; Julian I Schroeder; Joanne Chory; Joshua LaBaer; Marc Vidal; Pascal Braun; Joseph R Ecker
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-29       Impact factor: 11.205

3.  The transcription factor ERG recruits CCR4-NOT to control mRNA decay and mitotic progression.

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Journal:  Nat Struct Mol Biol       Date:  2016-06-06       Impact factor: 15.369

Review 4.  Interactome networks and human disease.

Authors:  Marc Vidal; Michael E Cusick; Albert-László Barabási
Journal:  Cell       Date:  2011-03-18       Impact factor: 41.582

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Review 6.  The emerging paradigm of network medicine in the study of human disease.

Authors:  Stephen Y Chan; Joseph Loscalzo
Journal:  Circ Res       Date:  2012-07-20       Impact factor: 17.367

7.  Multiplex single-molecule interaction profiling of DNA-barcoded proteins.

Authors:  Liangcai Gu; Chao Li; John Aach; David E Hill; Marc Vidal; George M Church
Journal:  Nature       Date:  2014-09-21       Impact factor: 49.962

8.  AMPylation of Rho GTPases subverts multiple host signaling processes.

Authors:  Andrew R Woolery; Xiaobo Yu; Joshua LaBaer; Kim Orth
Journal:  J Biol Chem       Date:  2014-10-09       Impact factor: 5.157

9.  Network Analysis of UBE3A/E6AP-Associated Proteins Provides Connections to Several Distinct Cellular Processes.

Authors:  Gustavo Martínez-Noël; Katja Luck; Simone Kühnle; Alice Desbuleux; Patricia Szajner; Jeffrey T Galligan; Diana Rodriguez; Leon Zheng; Kathleen Boyland; Flavian Leclere; Quan Zhong; David E Hill; Marc Vidal; Peter M Howley
Journal:  J Mol Biol       Date:  2018-02-06       Impact factor: 5.469

Review 10.  Edgotype: a fundamental link between genotype and phenotype.

Authors:  Nidhi Sahni; Song Yi; Quan Zhong; Noor Jailkhani; Benoit Charloteaux; Michael E Cusick; Marc Vidal
Journal:  Curr Opin Genet Dev       Date:  2013-11-26       Impact factor: 5.578

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