Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-08-22

AUTHORS

Fergal Casey, Nevan Krogan, Denis C Shields, Gerard Cagney

ABSTRACT

BackgroundGene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks.ResultsWe examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter.ConclusionWe present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin. More... »

PAGES

133

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-5-133

DOI

http://dx.doi.org/10.1186/1752-0509-5-133

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032283971

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21859460


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