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

Abstract

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 ofmore » 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.« less

Authors:
; ORCiD logo; ; ; ORCiD logo; ORCiD logo; ; ; ORCiD logo; ORCiD logo; ; ORCiD logo;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1492919
Alternate Identifier(s):
OSTI ID: 1491231; OSTI ID: 1525834
Report Number(s):
LA-UR-18-23278
Journal ID: ISSN 1553-7358; 10.1371/journal.pcbi.1006706
Grant/Contract Number:  
JDACS4C; AC52-06NA25396; 89233218CNA000001
Resource Type:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online) Journal Volume: 15 Journal Issue: 1; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science (PLoS)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science

Citation Formats

Erickson, Keesha E., Rukhlenko, Oleksii S., Shahinuzzaman, Md, Slavkova, Kalina P., Lin, Yen Ting, Suderman, Ryan, Stites, Edward C., Anghel, Marian, Posner, Richard G., Barua, Dipak, Kholodenko, Boris N., Hlavacek, William S., and Asthagiri, ed., Anand R. Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. United States: N. p., 2019. Web. doi:10.1371/journal.pcbi.1006706.
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Erickson, Keesha E., Rukhlenko, Oleksii S., Shahinuzzaman, Md, Slavkova, Kalina P., Lin, Yen Ting, Suderman, Ryan, Stites, Edward C., Anghel, Marian, Posner, Richard G., Barua, Dipak, Kholodenko, Boris N., Hlavacek, William S., & Asthagiri, ed., Anand R. Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. United States. https://doi.org/10.1371/journal.pcbi.1006706
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Erickson, Keesha E., Rukhlenko, Oleksii S., Shahinuzzaman, Md, Slavkova, Kalina P., Lin, Yen Ting, Suderman, Ryan, Stites, Edward C., Anghel, Marian, Posner, Richard G., Barua, Dipak, Kholodenko, Boris N., Hlavacek, William S., and Asthagiri, ed., Anand R. Thu . "Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor". United States. https://doi.org/10.1371/journal.pcbi.1006706.
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@article{osti_1492919,
title = {Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor},
author = {Erickson, Keesha E. and Rukhlenko, Oleksii S. and Shahinuzzaman, Md and Slavkova, Kalina P. and Lin, Yen Ting and Suderman, Ryan and Stites, Edward C. and Anghel, Marian and Posner, Richard G. and Barua, Dipak and Kholodenko, Boris N. and Hlavacek, William S. and Asthagiri, ed., Anand R.},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1006706},
journal = {PLoS Computational Biology (Online)},
number = 1,
volume = 15,
place = {United States},
year = {2019},
month = {1}
}
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https://doi.org/10.1371/journal.pcbi.1006706
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Figures / Tables:

Fig 1 Fig 1: IGF1R phosphotyrosine contact map in HeLa S3 cells. (A) Ligand-free and inactive IGF1R. Prior to crosslinking of dissimilar sites S1 and S2 of each IGF1R protomer in a dimer, the two kinase domains are not in contact and are thus unable to autophosphorylate receptor tyrosines. (B) IGF1R phosphotyrosinemore » interactions in HeLa S3 cells. Each IGF1R in an IGF1R dimer contains six autophosphorylation tyrosine sites, labeled “Y” followed by the site’s position within the polypeptide chain (according to UniProt numbering). The kinase domains of each IGF1R subunit are labeled accordingly and encompass the region spanning positions 999 to 1274. Autophosphorylation and dephosphorylation of activated (IGF1-crosslinked) receptors in a dimer occur at the tyrosine sites indicated. Signaling proteins containing SH2 and/or PTB domains are recruited to their cognate tyrosine sites in a phosphorylated IGF1R dimer as indicated by the colored lines that begin at a tyrosine site and end at the protein that is recruited to that site.« less
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