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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Trends in Geneticsarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Trends in Genetics
Article . 2002 . Peer-reviewed
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Protein–Protein Interactions: Protein interactions contribute to protein function

Authors: Pierre Legrain;

Protein–Protein Interactions: Protein interactions contribute to protein function

Abstract

Protein–Protein Interactions (A Molecular Cloning Manual) edited by Erica GolemisCold Spring Harbor Laboratory Press, 2001. £100.00 pbk (600 pages)ISBN 0 879 69628 1Proteins control and mediate many of the biological activities of cells. Hence, to gain an understanding of cellular function, the function of every protein must be understood, both in isolation and in the context of other interacting proteins. This view was established many years ago, but developments in genomics have led several scientists to invert this statement: if we knew most or all protein–protein interactions in a cell, the functions of most proteins would become clearer. Recently, we have seen the development of large-scale biological experiments that are technology-driven rather than hypothesis-driven. And, it has become evident that one can easily forget the main goal of biological achievement – understanding mechanisms of life – while concentrating on high-throughput technologies to populate biological databases. In this regard, protein–protein interactions are a special case: recent high-throughput technologies derived from a genetic assay in yeast, or from complex purification and mass spectrometric analyses have obscured the fact that protein associations have long been the focus of geneticists and biochemists. In the future, data raising from these various high-throughput technologies will be merged with knowledge extracted from the literature thanks to appropriate bioinformatics tools. Biologists will still perform hypothesis-driven science, but they will be building their hypothesis not only on their own expertise, but also by taking advantage of this incredible mass of data.Protein–Protein Interactions, edited by E. Golemis, reconciles modern large-scale technological approaches with more classical approaches to understanding proteins. In the first part of the book, the key importance of protein–protein interaction is illustrated in several examples such as signal transduction and human genetics. A brief description of MAPK pathway is given, emphasizing the role of interactions with scaffolding proteins in addition to signaling proteins. Protein interactions are also key to pharmacogenetic studies on DNA repair, and BRCA1 and BRCA2 protein complexes. Mutations in these proteins are associated with a diseased state and affect their ability to interact with other proteins.Several technical chapters cover in vitro approaches (glutathione-S-transferase pull-down, far westerns, coimmunoprecipitation and chemical crosslinking) and genetic assays (yeast and bacterial two-hybrid techniques, phage display and classical yeast suppressor screens). These are classical assays, but the rationale of experiments is well explained and protocols, often adapted from Sambrook and Russell [1xSambrook, J. and Russell, D. See all References[1], will certainly aid most researchers in their initial setting up of experiments. Also covered are techniques that are compatible with the physiology of the cell: fluorescence resonance energy transfer (FRET), proximity imaging of polypeptides tagged with green-fluorescent protein (GFP–PRIM) experiments to follow homotypic interactions, multicomplex purification followed by mass spectrometry analyses. Technologies are also discussed that make direct measurements of the affinity between proteins: atomic force microscopy, Biacore or acoustic biosensor analyses.In final section, the editor has asked to several scientists to describe their own work on protein–protein interactions, using specific variations of classical assays or developing new assays to achieve specific goals. Protease fingerprinting, various examples of pull-down experiments, catalytic antibodies, computational tools for protein interactions, and manipulation of protein networks for new therapeutic approaches are all described. For example, Chapter 10 illustrates the measurement of the activation state of the protein kinase C. This is done by a FRET experiment measuring the interaction between the phosphorylated protein at a specific residue and a specific antibody. In another example, multiprotein complexes are analyzed by mass spectrometry (Chapter 12), allowing characterization of the stoichiometry of complexes or the arrangement of the subunits.As a whole, the book is a comprehensive survey of ways to approach protein–protein interactions: the concepts underlying each technology are described and operational protocols are given. Experts might consider that some of the suggested protocols are not necessarily the most appropriate, but, in general, they will help the reader to design his/her experiment in a productive way. More importantly, the book often shows how protein interaction measurements help the understanding of biological mechanisms. This book develops an appreciation of the wide range of techniques addressing protein–protein interaction issues, and why some are better suited than others to answer a specific biological question.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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