MAR 24, 2016 10:00 AM PDT

BioPlex: a protein interaction network created from thousands of protein immunopurifications

Speakers
  • Professor in the Department of Cell Biology, Harvard Medical School
    Biography
      Steven Gygi received his Ph.D. from the University of Utah in the area of Pharmacology and Toxicology. He performed a postdoctoral fellowship with Ruedi Aebersold at the University of Washington in 1996. He joined the faculty at Harvard Medical School in 2000. He is currently a Professor in the Department of Cell Biology. Dr. Gygi is a leading technologist who uses mass spectrometry to answer fundamental questions in both normal and abnormal biology. He specializes in instrumentation advances for global cellular protein measurements.

    Abstract:
    DATE:  March 24, 2016
    TIME:   10am Pacific time, 1pm Eastern time

    Protein-protein interactions form a network whose structure drives cellular function and whose organization informs all biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions, 86% unknown, among 7,668 proteins. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. Network structure reveals 4 key features: 1) BioPlex subdivides into >300 communities, uniting proteins with shared function. 2) Interactions predict 2,968 associations among co-occurring Pfam domains. 3) Attributes including localization, biological process, and molecular function were determined for thousands of proteins - many uncharacterized. 4) BioPlex reveals interactions of biological or clinical significance. To demonstrate complementary studies inspired by BioPlex, we interrogated interactions of wild-type and mutant VAPB variants implicated in familial Amyotrophic Lateral Sclerosis. The network provides a framework for hypothesis generation and refinement as applied to protein function, mechanism, and activity.
     
    Learning Objectives:
    1. Learn how affinity purifications can be performed at scale and the associated caveats.
    2. Understand how an interaction network can be used to predict a protein’s cellular properties.

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