4.6 Article

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

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 15, 期 1, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1006706

关键词

-

资金

  1. Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program
  2. National Cancer Institute (NCI) of the National Institutes of Health (NIH)
  3. Center for Nonlinear Studies (CNLS) at Los Alamos National Laboratory (LANL)
  4. Laboratory-Directed Research and Development (LDRD) program at LANL
  5. NIH/NCI [R01CA197398]
  6. NIH/National Institute of General Medical Sciences (NIGMS) [R01GM111510]
  7. European Commission Horizon 2020 grant SmartNanoTox [686098]
  8. European Commission Horizon 2020 grant NanoCommons [731032]
  9. Marie Sklodowska-Curie Individual Fellowship SAMNets [750688]
  10. National Nuclear Security Administration (NNSA) of the DOE [DE-AC52-06NA25396]
  11. Marie Curie Actions (MSCA) [750688] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

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 K-D 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据