title: Use of mechanistic models to integrate and analyze multiple proteomic datasets.
abstract: Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment.
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri. Electronic address: estites@path.wustl.edu.
authors: Edward C
authors: EC
authors: Stites
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona.
authors: Meraj
authors: M
authors: Aziz
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut.
authors: Matthew S
authors: MS
authors: Creamer
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona.
authors: Daniel D
authors: DD
authors: Von Hoff
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona. Electronic address: rposner@tgen.org.
authors: Richard G
authors: RG
authors: Posner
authors: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico. Electronic address: wish@lanl.gov.
authors: William S
authors: WS
authors: Hlavacek
mesh_terms: D000818:Animals; D066264:Datasets as Topic; D066246:ErbB Receptors / Q000378:metabolism; D006367:HeLa Cells; D006801:Humans; D008954:Models, Biological; D020543:Proteome / Q000737:chemistry / Q000378:metabolism*; D055765:Shc Signaling Adaptor Proteins / Q000378:metabolism; D015398:Signal Transduction; D018909:src Homology Domains
chemical_list: D020543:Proteome; D055765:Shc Signaling Adaptor Proteins; D066246:ErbB Receptors