ArticleMolecular and cellular biology2014
Quantitative in vivo fluorescence cross-correlation analyses highlight the importance of competitive effects in the regulation of protein-protein interactions.
Article in Molecular and cellular biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 35 citations in OpenAlex.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-assisted simulation is a promising approach for clarifying complicated signaling networks. However, this approach is currently limited by a deficiency of kinetic parameters determined in living cells. To overcome this problem, we applied fluorescence cross-correlation spectrometry (FCCS) to measure dissociation constant (Kd) values of signaling molecule complexes in living cells (in vivo Kd). Among the pairs of fluorescent molecules tested, that of monomerized enhanced green fluorescent protein (mEGFP) and HaloTag-tetramethylrhodamine was most suitable for the measurement of in vivo Kd by FCCS. Using this pair, we determined 22 in vivo Kd values of signaling molecule complexes comprising the epidermal growth factor receptor (EGFR)-Ras-extracellular signal-regulated kinase (ERK) mitogen-activated protein (MAP) kinase pathway. With these parameters, we developed a kinetic simulation model of the EGFR-Ras-ERK MAP kinase pathway and uncovered a potential role played by stoichiometry in Shc binding to EGFR during the peak activations of Ras, MEK, and ERK. Intriguingly, most of the in vivo Kd values determined in this study were higher than the in vitro Kd values reported previously, suggesting the significance of competitive bindings inside cells. These in vivo Kd values will provide a sound basis for the quantitative understanding of signal transduction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.