Premium
Finding the Differential Interactome of active vs inactive Small Molecular Weight GTPases
Author(s) -
Peterson Tabitha,
Piper Robert C
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.619.17
Subject(s) - gtpase , ras superfamily , effector , gtp' , interactome , computational biology , gtp binding protein regulators , biology , function (biology) , sequence (biology) , g protein , microbiology and biotechnology , biochemistry , signal transduction , gene , enzyme
The family of Ras‐related small molecular weight GTP‐binding proteins encompasses over 160 members. Together, these members control myriad aspects of cell biological processes by binding effector proteins in a manner that is dependent on whether they are bound to GTP or GDP. Ras‐family GTPases are built on a conserved structural template that dictates the mode of nucleotide binding. This template provides the opportunity to create mutations within conserved residues that force the GTP‐binding protein into the GDP‐bound conformation, which is typically inactive, or a GTP‐bound conformation, which is typically “active” in the sense that it now binds more favorably to its effectors. Each member of the Ras superfamily is thought to have its own particular set of interacting proteins that allow it to define and execute its function. This specificity is accounted for with fairly subtle sequence differences, typically clustered on small regions on an overall highly structurally conserved skeleton. Here we show further development of the DEEPN approach, which uses next‐generation sequencing to compare yeast‐2 hybrid interactions in batch across protein variants to computationally define set of interacting proteins specific for one protein variant over another. We show the application of these improved tools and methods on a subset of Ras‐family GTPases, where we uncover interactions that are specific for the GTP‐bound vs GDP‐bound conformations. Moreover, we describe new libraries and other key reagents that streamline this approach so that it can be widely adapted by other laboratories. Finally, we show how these efforts can be applied in a undergraduate teaching lab to expose students to both molecular and genetic techniques as well as computational approaches. Support or Funding Information NSF Research Project Grant: 1517110