4.8 Article

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

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NATURE METHODS
卷 11, 期 1, 页码 94-+

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NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2733

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资金

  1. Dana-Farber/Harvard Cancer Center [P30-CA06516]
  2. Howard Hughes Medical Institute
  3. [P01-CA120964]
  4. [R01-GM067761]
  5. [R01DK088718]
  6. NATIONAL CANCER INSTITUTE [P01CA120964, P30CA006516] Funding Source: NIH RePORTER
  7. NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [R01AR057352] Funding Source: NIH RePORTER
  8. NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R01DK088718] Funding Source: NIH RePORTER
  9. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM067761] Funding Source: NIH RePORTER

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A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

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