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A protein interaction network of the malaria parasite Plasmodium falciparum

Author

Listed:
  • Douglas J. LaCount

    (Howard Hughes Medical Institute
    University of Washington)

  • Marissa Vignali

    (University of Washington)

  • Rakesh Chettier

    (Prolexys Pharmaceuticals, Inc.)

  • Amit Phansalkar

    (Prolexys Pharmaceuticals, Inc.)

  • Russell Bell

    (Prolexys Pharmaceuticals, Inc.)

  • Jay R. Hesselberth

    (University of Washington)

  • Lori W. Schoenfeld

    (Howard Hughes Medical Institute
    University of Washington)

  • Irene Ota

    (Prolexys Pharmaceuticals, Inc.)

  • Sudhir Sahasrabudhe

    (Prolexys Pharmaceuticals, Inc.)

  • Cornelia Kurschner

    (Prolexys Pharmaceuticals, Inc.)

  • Stanley Fields

    (Howard Hughes Medical Institute
    University of Washington)

  • Robert E. Hughes

    (Prolexys Pharmaceuticals, Inc.
    Buck Institute)

Abstract

What makes a parasite tick A powerful approach for understanding protein function is to identify which proteins bind to each other, as protein complexes are at the heart of most biological processes. Protein–protein interactions have now been mapped for one quarter of the malaria parasite's proteins. This large data set sheds new light on how parasites infect red blood cells and will be a vital tool for the development of new antimalarial drugs and vaccines. The primary data are freely available on the PlasmoDB database. Suthram et al. have used this new resource and find that the Plasmodium network has significantly less cross-species similarity than other eukaryotes. Its novel life style is reflected in a novel protein network, which therefore has a good chance of providing drug targets unique to the malaria parasite.

Suggested Citation

  • Douglas J. LaCount & Marissa Vignali & Rakesh Chettier & Amit Phansalkar & Russell Bell & Jay R. Hesselberth & Lori W. Schoenfeld & Irene Ota & Sudhir Sahasrabudhe & Cornelia Kurschner & Stanley Field, 2005. "A protein interaction network of the malaria parasite Plasmodium falciparum," Nature, Nature, vol. 438(7064), pages 103-107, November.
    • Handle: RePEc:nat:nature:v:438:y:2005:i:7064:d:10.1038_nature04104
    DOI: 10.1038/nature04104
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    Cited by:

    1. Prajwal Devkota & Matt C Danzi & Stefan Wuchty, 2018. "Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-14, May.
    2. Luis P Fernandes & Alessia Annibale & Jens Kleinjung & Anthony C C Coolen & Franca Fraternali, 2010. "Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-14, August.
    3. Alalwan, Najlaa & Arenas, Alex & Estrada, Ernesto, 2019. "“Melting” of complex networks. A mathematical model of complex networks resilience to external stress," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.

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