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Controllability in protein interaction networks.

ABSTRACT: Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched with essential, cancer-related, and virus-targeted genes. Their central position allowed MDSet proteins to connect protein complexes and to have a higher impact on network resilience than hub proteins. As for their involvement in regulatory functions, MDSet proteins were enriched with transcription factors and protein kinases and were significantly involved in bottleneck interactions, regulatory links, phosphorylation events, and genetic interactions.

SUBMITTER: Wuchty S

PROVIDER: S-EPMC4024882 | biostudies-other | 2014 May

REPOSITORIES: biostudies-other

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Controllability in protein interaction networks.

Wuchty Stefan S

Proceedings of the National Academy of Sciences of the United States of America 20140428 19

Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched ...[more]

PMID: 24778220

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Project description:BackgroundHost factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.ResultsBoth topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.ConclusionsSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Dataset Information

Controllability in protein interaction networks.

Publications

Controllability in protein interaction networks.

Similar Datasets

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