TY  - JOUR
A1  - Cuperus, Josh T.
A1  - Groves, Benjamin
A1  - Kuchina, Anna
A1  - Rosenberg, Alexander B.
A1  - Jojic, Nebojsa
A1  - Fields, Stanley
A1  - Seelig, Georg
T1  - Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences
Y1  - 2017/12/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 2015 
EP  - 2024 
DO  - 10.1101/gr.224964.117 
VL  - 27 
IS  - 12 
UR  - http://genome.cshlp.org/content/27/12/2015.abstract 
N2  - 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. 
ER  -