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Protocol for intervention-free quantification of protein turnover rate by steady-state modeling

Protein turnover rate is difficult to obtain experimentally. This protocol shows how to mathematically model turnover rates in an intervention-free manner given the ability to quantify mRNA and protein expression from initiation to homeostasis. This approach can be used to calculate production and d...

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Autores principales: Marcotti, Stefania, Sánchez-Sánchez, Besaiz Jose, Serna-Morales, Eduardo, Dragu, Anca, Díaz-de-la-Loza, María-del-Carmen, Matsubayashi, Yutaka, Stramer, Brian Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988239/
https://www.ncbi.nlm.nih.gov/pubmed/33786460
http://dx.doi.org/10.1016/j.xpro.2021.100377
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author Marcotti, Stefania
Sánchez-Sánchez, Besaiz Jose
Serna-Morales, Eduardo
Dragu, Anca
Díaz-de-la-Loza, María-del-Carmen
Matsubayashi, Yutaka
Stramer, Brian Marc
author_facet Marcotti, Stefania
Sánchez-Sánchez, Besaiz Jose
Serna-Morales, Eduardo
Dragu, Anca
Díaz-de-la-Loza, María-del-Carmen
Matsubayashi, Yutaka
Stramer, Brian Marc
author_sort Marcotti, Stefania
collection PubMed
description Protein turnover rate is difficult to obtain experimentally. This protocol shows how to mathematically model turnover rates in an intervention-free manner given the ability to quantify mRNA and protein expression from initiation to homeostasis. This approach can be used to calculate production and degradation rates and to infer protein half-life. This model was successfully employed to quantify turnover during Drosophila embryogenesis, and we hypothesize that it will be applicable to diverse in vivo or in vitro systems. For complete details on the use and execution of this protocol, please refer to Matsubayashi et al. (2020).
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spelling pubmed-79882392021-03-29 Protocol for intervention-free quantification of protein turnover rate by steady-state modeling Marcotti, Stefania Sánchez-Sánchez, Besaiz Jose Serna-Morales, Eduardo Dragu, Anca Díaz-de-la-Loza, María-del-Carmen Matsubayashi, Yutaka Stramer, Brian Marc STAR Protoc Protocol Protein turnover rate is difficult to obtain experimentally. This protocol shows how to mathematically model turnover rates in an intervention-free manner given the ability to quantify mRNA and protein expression from initiation to homeostasis. This approach can be used to calculate production and degradation rates and to infer protein half-life. This model was successfully employed to quantify turnover during Drosophila embryogenesis, and we hypothesize that it will be applicable to diverse in vivo or in vitro systems. For complete details on the use and execution of this protocol, please refer to Matsubayashi et al. (2020). Elsevier 2021-03-18 /pmc/articles/PMC7988239/ /pubmed/33786460 http://dx.doi.org/10.1016/j.xpro.2021.100377 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Marcotti, Stefania
Sánchez-Sánchez, Besaiz Jose
Serna-Morales, Eduardo
Dragu, Anca
Díaz-de-la-Loza, María-del-Carmen
Matsubayashi, Yutaka
Stramer, Brian Marc
Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title_full Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title_fullStr Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title_full_unstemmed Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title_short Protocol for intervention-free quantification of protein turnover rate by steady-state modeling
title_sort protocol for intervention-free quantification of protein turnover rate by steady-state modeling
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988239/
https://www.ncbi.nlm.nih.gov/pubmed/33786460
http://dx.doi.org/10.1016/j.xpro.2021.100377
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