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Algorithms for protein interaction networks.

M Lappe1, L Holm

  • 1Max-Planck Institute of Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.

Biochemical Society Transactions
|May 27, 2005
PubMed
Summary
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This study presents computational methods to predict protein structure and function using protein interaction networks. Algorithms are developed to extract biological pathways and build interactome maps efficiently.

Area of Science:

  • Proteomics
  • Systems Biology
  • Bioinformatics

Background:

  • The postgenomic era faces the challenge of characterizing all genes and their products.
  • Large-scale studies of protein interactions are now feasible through experimental and computational techniques.

Purpose of the Study:

  • To present approaches for inferring protein structure, function, and pathways using interaction data.
  • To develop algorithms for extracting signal-transduction pathways from biological literature.
  • To formulate a strategy for rapid and reliable interactome scaffold construction.

Main Methods:

  • Modeling interaction networks as graphs to represent gene function.
  • Utilizing graph architecture differences from random networks for predictive purposes.

Related Experiment Videos

  • Developing algorithms for pathway extraction from text-mined literature data.

    • Main Results:

    • Protein function and structure prediction without sequence similarity by matching interaction patterns.
    • Successful extraction of signal-transduction pathways from noisy network data.
    • Formulation of an algorithmic strategy for efficient interactome scaffold building.

    Conclusions:

    • Protein interaction networks provide valuable signals for predicting protein attributes.
    • Computational approaches can effectively deconstruct complex networks into functional pathways.
    • An integrated strategy can accelerate the construction of a reliable protein interactome.