Essentiality and centrality in protein interaction networks revisited

Sawsan Khuri, Stefan Wuchty

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Background: Minimum dominating sets (MDSet) of protein interaction networks allow the control of underlying protein interaction networks through their topological placement. While essential proteins are enriched in MDSets, we hypothesize that the statistical properties of biological functions of essential genes are enhanced when we focus on essential MDSet proteins (e-MDSet). Results: Here, we determined minimum dominating sets of proteins (MDSet) in interaction networks of E. coli, S. cerevisiae and H. sapiens, defined as subsets of proteins whereby each remaining protein can be reached by a single interaction. We compared several topological and functional parameters of essential, MDSet, and essential MDSet (e-MDSet) proteins. In particular, we observed that their topological placement allowed e-MDSet proteins to provide a positive correlation between degree and lethality, connect more protein complexes, and have a stronger impact on network resilience than essential proteins alone. In comparison to essential proteins we further found that interactions between e-MDSet proteins appeared more frequently within complexes, while interactions of e-MDSet proteins between complexes were depleted. Finally, these e-MDSet proteins classified into functional groupings that play a central role in survival and adaptability. Conclusions: The determination of e-MDSet of an organism highlights a set of proteins that enhances the enrichment signals of biological functions of essential proteins. As a consequence, we surmise that e-MDSets may provide a new method of evaluating the core proteins of an organism.

Original languageEnglish (US)
Article number109
JournalBMC Bioinformatics
Issue number1
StatePublished - Apr 1 2015


  • Essential proteins
  • PPI
  • Protein function
  • Protein network

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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