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Deciphering mRNA Sequence Determinants of Protein Production Rate.
Szavits-Nossan, Juraj; Ciandrini, Luca; Romano, M Carmen.
Afiliação
  • Szavits-Nossan J; SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom.
  • Ciandrini L; L2C, Université de Montpellier, CNRS, Montpellier, France and DIMNP, Université de Montpellier, CNRS, Montpellier, France.
  • Romano MC; SUPA, Institute for Complex Systems and Mathematical Biology, Department of Physics, Aberdeen AB24 3UE, United Kingdom and Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB24 3FX, United Kingdom.
Phys Rev Lett ; 120(12): 128101, 2018 Mar 23.
Article em En | MEDLINE | ID: mdl-29694095
One of the greatest challenges in biophysical models of translation is to identify coding sequence features that affect the rate of translation and therefore the overall protein production in the cell. We propose an analytic method to solve a translation model based on the inhomogeneous totally asymmetric simple exclusion process, which allows us to unveil simple design principles of nucleotide sequences determining protein production rates. Our solution shows an excellent agreement when compared to numerical genome-wide simulations of S. cerevisiae transcript sequences and predicts that the first 10 codons, which is the ribosome footprint length on the mRNA, together with the value of the initiation rate, are the main determinants of protein production rate under physiological conditions. Finally, we interpret the obtained analytic results based on the evolutionary role of the codons' choice for regulating translation rates and ribosome densities.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biossíntese de Proteínas / RNA Mensageiro / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biossíntese de Proteínas / RNA Mensageiro / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Reino Unido