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Genome Res. 2017 Dec;27(12):2015-2024. doi: 10.1101/gr.224964.117. Epub 2017 Nov 2.

Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences.

Author information

1
Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
2
Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA.
3
Department of Electrical Engineering, University of Washington, Seattle, Washington 98195, USA.
4
Microsoft Research, Seattle, Washington 98195, USA.
5
Department of Medicine, University of Washington, Seattle, Washington 98195, USA.
6
Department of Computer Science & Engineering, University of Washington, Seattle, Washington 98195, USA.

Abstract

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5' UTRs as well as native S. cerevisiae 5' UTRs. The model additionally was used to computationally evolve highly active 5' UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.

PMID:
29097404
PMCID:
PMC5741052
DOI:
10.1101/gr.224964.117
[Indexed for MEDLINE]
Free PMC Article

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