iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/23635424
Protein-protein interaction networks: probing disease mechanisms using model systems - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2013 Apr 30;5(4):37.
doi: 10.1186/gm441. eCollection 2013.

Protein-protein interaction networks: probing disease mechanisms using model systems

Affiliations
Review

Protein-protein interaction networks: probing disease mechanisms using model systems

Uros Kuzmanov et al. Genome Med. .

Abstract

Protein-protein interactions (PPIs) and multi-protein complexes perform central roles in the cellular systems of all living organisms. In humans, disruptions of the normal patterns of PPIs and protein complexes can be causative or indicative of a disease state. Recent developments in the biological applications of mass spectrometry (MS)-based proteomics have expanded the horizon for the application of systematic large-scale mapping of physical interactions to probe disease mechanisms. In this review, we examine the application of MS-based approaches for the experimental analysis of PPI networks and protein complexes, focusing on the different model systems (including human cells) used to study the molecular basis of common diseases such as cancer, cardiomyopathies, diabetes, microbial infections, and genetic and neurodegenerative disorders.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Schematic representation of alterations in protein-protein interactions under pathological conditions. A highly simplified view of how disease-related proteins can drive disease processes by altering individual protein complexes and protein network dynamics. They can replace and co-opt 'steady state' protein complex components or can interfere with normal protein network interactions. By identifying proteins in complex with known disease-related proteins, interacting members of the complex can be then be designated as candidates with a role in pathological progression.
Figure 2
Figure 2
The isolation of protein complexes and the identification of components. (a) Approaches for the isolation of protein complexes. Prior to the MS-based identification of individual polypeptides, physically associated protein complexes can be isolated from crude extracts using either: (i) co-purification (AP) of stably associated protein interactors of a tagged bait protein that is expressed in a cell; (ii) antibody-based pull-down (co-IP) of complexes containing a protein target of interest; or (iii) biochemical co-fractionation of protein complexes using native chromatographic separation. (b) Liquid chromatography (LC)-MS-based identification is then performed to characterize the co-purifying protein complex components. (i) Proteins are initially cleaved by a protease (normally trypsin) to generate peptides, which are subjected to reverse-phase LC separation followed by electrospray ionization prior to MS analysis. (ii) In the first mass analyzer (MS1) charged peptides with the highest intensity are sequentially selected (one by one) for collision-induced fragmentation. The second mass analyzer (MS2) records the mass of peptide fragments (with signal peaks expressed as mass to charge ratios (m/z)). (iii) MS1 and MS2 data for each peptide are then used together to search a cognate protein sequence database to produce a list of confidently identified peptides and proteins.

Similar articles

Cited by

References

    1. Bonetta L. Protein-protein interactions: interactome under construction. Nature. 2010;468:851–854. doi: 10.1038/468851a. - DOI - PubMed
    1. Venkatesan K, Rual JF, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh KI, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet AS, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J. et al.An empirical framework for binary interactome mapping. Nat Methods. 2009;6:83–90. doi: 10.1038/nmeth.1280. - DOI - PMC - PubMed
    1. Brown CJ, Lain S, Verma CS, Fersht AR, Lane DP. Awakening guardian angels: drugging the p53 pathway. Nat Rev Cancer. 2009;9:862–873. doi: 10.1038/nrc2763. - DOI - PubMed
    1. Joerger AC, Fersht AR. Structure-function-rescue: the diverse nature of common p53 cancer mutants. Oncogene. 2007;26:2226–2242. doi: 10.1038/sj.onc.1210291. - DOI - PubMed
    1. Brooke MA, Nitoiu D, Kelsell DP. Cell-cell connectivity: desmosomes and disease. J Pathol. 2012;226:158–171. doi: 10.1002/path.3027. - DOI - PubMed

LinkOut - more resources