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Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
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Protein complex prediction via dense subgraphs and false positive analysis.

Cecilia Hernandez1,2, Carlos Mella1, Gonzalo Navarro2

  • 1Computer Science, University of Concepción, Concepción, Chile.

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|September 23, 2017
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Summary

We developed an efficient algorithm to discover protein complexes by analyzing protein-protein interaction networks. Our method identifies many structurally validated complexes missed by other tools, improving biological discovery.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Systems Biology

Background:

  • Protein complexes are crucial for biological functions.
  • Identifying these complexes aids in understanding biological processes and predicting protein functions.
  • Existing computational methods for protein complex detection have limitations in speed and accuracy, and often lack false positive analysis.

Purpose of the Study:

  • To propose an effective and efficient algorithm for discovering protein complexes based on highly connected subgraphs.
  • To improve the accuracy and reduce the processing time of protein complex detection.
  • To analyze false positives and validate predictions against structural databases.

Main Methods:

  • Developed a novel algorithm that transforms protein-protein interaction (PPI) networks into directed acyclic graphs to reduce search space.
  • Applied the algorithm to weighted and unweighted PPI networks from Saccharomyces cerevisiae and Homo sapiens.
  • Compared the algorithm's performance against state-of-the-art methods using biological metrics and gold standards.
  • Validated predicted false positives against the Protein Data Bank in Europe (PDBe) database.

Main Results:

  • The proposed algorithm is effective and efficient in discovering protein complexes.
  • A significant number of predicted false positives (over 50 for yeast, over 300 for human) were structurally characterized and documented in PDBe.
  • Some of these validated complexes are recognized in the Periodic Table of Protein Complexes.

Conclusions:

  • The developed algorithm enhances protein complex detection by improving efficiency and accuracy.
  • The validation against PDBe demonstrates the biological relevance of complexes identified by the algorithm, even those initially flagged as false positives.
  • This work provides a valuable tool for guiding future biological research and structural studies of protein complexes.