title: A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, differential signaling, and relevance to oncogenic transformations.
abstract: We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Yingting
authors.initials: Y
authors.lastname: Liu
authors.forename: Jeremy
authors.initials: J
authors.lastname: Purvis
authors.forename: Andrew
authors.initials: A
authors.lastname: Shih
authors.forename: Joshua
authors.initials: J
authors.lastname: Weinstein
authors.forename: Neeraj
authors.initials: N
authors.lastname: Agrawal
authors.forename: Ravi
authors.initials: R
authors.lastname: Radhakrishnan
mesh_terms: D000818:Animals; D002471:Cell Transformation, Neoplastic / Q000378:metabolism*; D003198:Computer Simulation; D004815:Epidermal Growth Factor / Q000378:metabolism*; D066246:ErbB Receptors / Q000378:metabolism*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction
chemical_list: D004815:Epidermal Growth Factor; D066246:ErbB Receptors