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Functional coherence in domain interaction networks.

Jayesh Pandey1, Mehmet Koyutürk, Shankar Subramaniam

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu

Bioinformatics (Oxford, England)
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

Domain-domain interactions (DDIs) offer a more biologically intuitive way to understand protein function than protein-protein interactions (PPIs). Our new measure shows DDIs correlate better with functional similarity, especially structurally determined ones.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein-protein interactions (PPIs) data is often noisy, incomplete, and static, hindering functional information extraction.
  • Proteins function through domains, making domain-domain interactions (DDIs) a promising network analysis abstraction.
  • Existing methods struggle to compare functional coherence across different biological entities like proteins and domains.

Purpose of the Study:

  • To formally investigate the relationship between functional coherence and topological proximity in PPI and DDI networks.
  • To develop a statistically motivated measure for assessing functional similarity between biological entities.
  • To compare the effectiveness of DDIs versus PPIs for functional characterization and network modularization.

Main Methods:

  • Established essential attributes for admissible functional coherence measures.
  • Developed a novel, statistically motivated functional similarity measure accounting for specificity and attribute distribution.
  • Evaluated the measure on diverse PPI and DDI datasets (high-throughput, predicted, structural, inferred) across organisms.

Main Results:

  • The proposed measure captures the functional similarity-network proximity relationship more intuitively than existing measures.
  • Network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks.
  • Structurally determined DDIs exhibit stronger functional relevance compared to computationally inferred DDIs.

Conclusions:

  • DDIs provide a more biologically relevant abstraction for functional characterization than traditional PPI networks.
  • The developed functional similarity measure offers a statistically sound and interpretable approach for network analysis.
  • Further focused investigation into DDIs is warranted for improved understanding of biological networks and function.