Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Journal Article · 17 January 2019 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1006706 · OSTI ID:1492919

Erickson, Keesha E. ^[1]; Rukhlenko, Oleksii S.  ^[2]; Shahinuzzaman, Md ^[3]; Slavkova, Kalina P. ^[1]; Lin, Yen Ting  ^[1]; Suderman, Ryan T.  ^[1]; Stites, Edward C. ^[4]; Anghel, Marian  ^[1]; Posner, Richard G.  ^[5]; Barua, Dipak  ^[3]; Kholodenko, Boris N. ^[2]; Hlavacek, William Scott  ^[1]; Asthagiri, Anand R. ^[6]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Univ. College Dublin, Dublin (Ireland)
3. Univ. of Missouri Science and Technology, Rolla, MO (United States)
4. The Salk Inst. for Biological Studies, La Jolla, CA (United States)
5. Northern Arizona Univ., Flagstaff, AZ (United States)
6. Northeastern Univ., Boston, MA (United States)

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001; JDACS4C; AC52-06NA25396
• OSTI ID: 1492919
• Alternate ID(s): OSTI ID: 1491231; OSTI ID: 1525834
• Report Number(s): LA-UR-18-23278
• Journal Information: PLoS Computational Biology (Online), Vol. 15, Issue 1; ISSN 1553-7358
• Publisher: Public Library of ScienceCopyright Statement
• Country of Publication: United States
• Language: English