Structural relatedness via flow networks in protein sequence space

Zakharia M. Frenkel, Zeev M. Frenkel, Edward N. Trifonov, Sagi Snir

Research output: Contribution to journalArticlepeer-review


A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.

Original languageEnglish
Pages (from-to)438-444
Number of pages7
JournalJournal of Theoretical Biology
Issue number3
StatePublished - 7 Oct 2009

Bibliographical note

Funding Information:
The work has been supported by the Center for Complexity Science Grant GR2006-018.


  • Network analysis
  • Protein sequence analysis
  • Protein structure prediction
  • Sequence annotation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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