New Perspectives in Predicting Membrane Protein-Protein Interactions

Zhang, Kelvin X.

doi:10.5772/8052

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Because the assumption is that interactions that are present multiple times are more reliable (given higher weight) a bias is introduced due to over-representation by popular methods for PPI identification, such as the yeast two-hybrid system (Y2H) [ 11 , 12 ] and affinity capture-mass spectrometry (affinity capture-MS) [ 13 , 14 ]. The Y2H method requires that the two proteins localize to the nucleus; however, integral membrane proteins expressed in an aqueous nuclear environment may aggregate or misfold [ 11 , 12 , 15 ], resulting in depletion of these proteins from the final interaction dataset. Also, the datasets derived from affinity capture-MS can be biased against integral membrane proteins, because biochemical purifications require detergents to isolate proteins away from the lipid bilayer [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Removing bias against membrane proteins in interaction networks

Brito

Andrews

2011

BMC Syst Biol

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BackgroundCellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.ResultsTo overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast Saccharomyces cerevisiae revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.ConclusionsWe show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.

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