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Nat Struct Mol Biol. 2018 Jul;25(7):577-582. doi: 10.1038/s41594-018-0080-2. Epub 2018 Jul 2.

Accurate design of translational output by a neural network model of ribosome distribution.

Author information

1
Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
2
Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
3
California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA.
4
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
5
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
6
California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA. lareau@berkeley.edu.

Abstract

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output.

PMID:
29967537
DOI:
10.1038/s41594-018-0080-2

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