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.

Figure 1

Interactions considered in a HeLa cell-specific version of a model for early events in EGFR signaling. Squares at left represent sites of EGFR autophosphorylation, squares at right represent SH2/PTB domain-containing proteins expressed in HeLa cells, and lines connecting squares represent interactions mapped and characterized experimentally. The squares at right are drawn such that area is proportional to the logarithm of measured protein copy number, and lines are drawn such that thickness is proportional to the logarithm of 1/K_D. The squares at right are ordered from top to bottom according to predicted peak level of association with EGFR after addition of EGF. The most highly recruited protein, SHC1, is represented at the top. To see this figure in color, go online.

Figure 2

Heatmap summarizing predicted time courses of protein recruitment for a HeLa cell-specific version of a model for early events in EGFR signaling. Simulation data have been scaled such that the minimum and maximum values for each time course are 0 and 1, respectively. The results shown were obtained from a stochastic simulation of EGFR signaling in a single cell. To see this figure in color, go online.

Figure 3

The predicted peak level of recruitment of a protein to EGFR in HeLa cells correlates with the protein’s highest affinity interaction with EGFR (top), its copy number (middle), and the number of phosphotyrosines in EGFR with which it interacts (bottom). However, the correlations are imperfect. The colored points correspond to SHC1 (red), GRB2 (orange), YES1 (yellow), RASA1 (green), VAV2 (blue), and PTPN11 (purple). To see this figure in color, go online.