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Protein interaction maps for complete genomes based on gene fusion events

Author

Listed:
  • Anton J. Enright

    (Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation)

  • Ioannis Iliopoulos

    (Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation)

  • Nikos C. Kyrpides

    (Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation
    Integrated Genomics Inc.)

  • Christos A. Ouzounis

    (Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation)

Abstract

A large-scale effort to measure, detect and analyse protein–protein interactions using experimental methods is under way1,2. These include biochemistry such as co-immunoprecipitation or crosslinking, molecular biology such as the two-hybrid system or phage display, and genetics such as unlinked noncomplementing mutant detection3. Using the two-hybrid system4, an international effort to analyse the complete yeast genome is in progress5. Evidently, all these approaches are tedious, labour intensive and inaccurate6. From a computational perspective, the question is how can we predict that two proteins interact from structure or sequence alone. Here we present a method that identifies gene-fusion events in complete genomes, solely based on sequence comparison. Because there must be selective pressure for certain genes to be fused over the course of evolution, we are able to predict functional associations of proteins. We show that 215 genes or proteins in the complete genomes of Escherichia coli, Haemophilus influenzae and Methanococcus jannaschii are involved in 64 unique fusion events. The approach is general, and can be applied even to genes of unknown function.

Suggested Citation

  • Anton J. Enright & Ioannis Iliopoulos & Nikos C. Kyrpides & Christos A. Ouzounis, 1999. "Protein interaction maps for complete genomes based on gene fusion events," Nature, Nature, vol. 402(6757), pages 86-90, November.
    • Handle: RePEc:nat:nature:v:402:y:1999:i:6757:d:10.1038_47056
    DOI: 10.1038/47056
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    Citations

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    Cited by:

    1. Chuanhua Xing & David B Dunson, 2011. "Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions," PLOS Computational Biology, Public Library of Science, vol. 7(7), pages 1-10, July.
    2. Sayed Mohammad Ebrahim Sahraeian & Byung-Jun Yoon, 2012. "A Network Synthesis Model for Generating Protein Interaction Network Families," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-14, August.
    3. Xue Wang & Yuejin Wu & Rujing Wang & Yuanyuan Wei & Yuanmiao Gui, 2019. "A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-12, June.
    4. Chittibabu Guda & Brian R King & Lipika R Pal & Purnima Guda, 2009. "A Top-Down Approach to Infer and Compare Domain-Domain Interactions across Eight Model Organisms," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-15, March.
    5. Vijaykumar Yogesh Muley & Akash Ranjan, 2012. "Effect of Reference Genome Selection on the Performance of Computational Methods for Genome-Wide Protein-Protein Interaction Prediction," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-13, July.
    6. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    7. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.
    8. Beatriz García-Jiménez & David Juan & Iakes Ezkurdia & Eduardo Andrés-León & Alfonso Valencia, 2010. "Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-10, April.
    9. Saeid Rasti & Chrysafis Vogiatzis, 2019. "A survey of computational methods in protein–protein interaction networks," Annals of Operations Research, Springer, vol. 276(1), pages 35-87, May.

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