A Combined Experimental and Computational Strategy to Define Protein Interaction Networks for Peptide Recognition Modules

Amy H.Y. Tong(University of Toronto), Becky Drees(University of Washington), Giuliano Nardelli(University of Rome Tor Vergata), Gary D. Bader(Mount Sinai Hospital), Barbara Brannetti(University of Rome Tor Vergata), Luisa Castagnoli(University of Rome Tor Vergata), Marie Evangelista(Queen's University), Silvia Ferracuti(University of Rome Tor Vergata), Bryce Nelson(Queen's University), Serena Paoluzi(University of Rome Tor Vergata), Michele Quondam(University of Rome Tor Vergata), Adriana Zucconi(University of Rome Tor Vergata), Christopher W.V. Hogue(Mount Sinai Hospital), Stanley Fields(Howard Hughes Medical Institute), Charles Boone(University of Toronto), Gianni Cesareni(University of Rome Tor Vergata)
Science
January 11, 2002
10.1126/science.1064987
Cited by 713Open Access
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Abstract

Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation.

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