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

Cuperus, J. T.; Groves, B.; Kuchina, A.; Rosenberg, A. B.; Jojic, N.; Fields, S.; Seelig, G.

doi:10.1101/137547

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bioRxiv · 10.1101/137547

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

Cuperus, J. T.·Groves, B.·Kuchina, A.·Rosenberg, A. B.·Jojic, N.·Fields, S.·Seelig, G.

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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 translational efficiency 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 translation 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 translational efficiency 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 translating 5 UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model.

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Cuperus, J. T., Groves, B., Kuchina, A., Rosenberg, A. B., Jojic, N., Fields, S., Seelig, G.. 2017-05-12. Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences. https://doi.org/10.1101/137547

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