Literature DB >> 29957823

Discovering cellular protein-protein interactions: Technological strategies and opportunities.

Kevin Titeca1,2, Irma Lemmens1,2, Jan Tavernier1,2, Sven Eyckerman1,2.   

Abstract

The analysis of protein interaction networks is one of the key challenges in the study of biology. It connects genotypes to phenotypes, and disruption often leads to diseases. Hence, many technologies have been developed to study protein-protein interactions (PPIs) in a cellular context. The expansion of the PPI technology toolbox however complicates the selection of optimal approaches for diverse biological questions. This review gives an overview of the binary and co-complex technologies, with the former evaluating the interaction of two co-expressed genetically tagged proteins, and the latter only needing the expression of a single tagged protein or no tagged proteins at all. Mass spectrometry is crucial for some binary and all co-complex technologies. After the detailed description of the different technologies, the review compares their unique specifications, advantages, disadvantages, and applicability, while highlighting opportunities for further advancements.
© 2018 Wiley Periodicals, Inc.

Keywords:  binary interactomics; co-complex interactomics; interactomics technologies; protein-protein interactions

Mesh:

Year:  2018        PMID: 29957823     DOI: 10.1002/mas.21574

Source DB:  PubMed          Journal:  Mass Spectrom Rev        ISSN: 0277-7037            Impact factor:   10.946


  18 in total

1.  Fluorogenic Photoaffinity Labeling of Proteins in Living Cells.

Authors:  Tewoderos M Ayele; Steve D Knutson; Satheesh Ellipilli; Hyun Hwang; Jennifer M Heemstra
Journal:  Bioconjug Chem       Date:  2019-04-17       Impact factor: 4.774

Review 2.  Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks.

Authors:  Daniela Salas; R Greg Stacey; Mopelola Akinlaja; Leonard J Foster
Journal:  Mol Cell Proteomics       Date:  2019-12-02       Impact factor: 5.911

3.  Analyzing large scale gene expression data in colorectal cancer reveals important clues; CLCA1 and SELENBP1 downregulated in CRC not in normal and not in adenoma.

Authors:  Fariborz Asghari Alashti; Bahram Goliaei; Zarrin Minuchehr
Journal:  Am J Cancer Res       Date:  2022-01-15       Impact factor: 6.166

4.  Application of Skyline for Analysis of Protein-Protein Interactions In Vivo.

Authors:  Arman Kulyyassov
Journal:  Molecules       Date:  2021-11-26       Impact factor: 4.411

5.  De novo designed peptides for cellular delivery and subcellular localisation.

Authors:  Guto G Rhys; Jessica A Cross; William M Dawson; Harry F Thompson; Sooruban Shanmugaratnam; Nigel J Savery; Mark P Dodding; Birte Höcker; Derek N Woolfson
Journal:  Nat Chem Biol       Date:  2022-07-14       Impact factor: 16.174

6.  Identification of all-against-all protein-protein interactions based on deep hash learning.

Authors:  Yue Jiang; Yuxuan Wang; Lin Shen; Donald A Adjeroh; Zhidong Liu; Jie Lin
Journal:  BMC Bioinformatics       Date:  2022-07-08       Impact factor: 3.307

Review 7.  Applications and advancements of FT-ICR-MS for interactome studies.

Authors:  Juan D Chavez; Sung-Gun Park; Jared P Mohr; James E Bruce
Journal:  Mass Spectrom Rev       Date:  2020-12-08       Impact factor: 10.946

Review 8.  Protein Interaction Network Biology in Neuroscience.

Authors:  Avik Basu; Peter Ea Ash; Benjamin Wolozin; Andrew Emili
Journal:  Proteomics       Date:  2020-12-29       Impact factor: 3.984

9.  A protein-protein interaction map of the TNF-induced NF-κB signal transduction pathway.

Authors:  Emmy Van Quickelberghe; Delphine De Sutter; Geert van Loo; Sven Eyckerman; Kris Gevaert
Journal:  Sci Data       Date:  2018-12-18       Impact factor: 6.444

10.  Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization.

Authors:  Joanna Winkler; Evelien Mylle; Andreas De Meyer; Benjamin Pavie; Julie Merchie; Peter Grones; Dani L Van Damme
Journal:  Plant Cell       Date:  2021-05-31       Impact factor: 11.277

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