Model-driven generation of artificial yeast promoters

Abstract

Promoters play a central role in controlling gene regulation; however, a small set of promoters is used for most genetic construct design in the yeast Saccharomyces cerevisiae. Generating and utilizing models that accurately predict protein expression from promoter sequences would enable rapid generation of novel useful promoters and facilitate synthetic biology efforts in this model organism. We measured the gene expression activity of over 675,000 unique sequences in a constitutive promoter library, and over 327,000 sequences in an inducible promoter library. Training an ensemble of convolutional neural networks jointly on the two datasets enabled very high (R2 > 0.79) predictive accuracies on multiple sequence-activity prediction tasks. We developed model-guided design strategies which yielded large, sequence-diverse sets of novel promoters exhibiting activities similar to current best-in-class sequences. In addition to providing large sets of new promoters, our results show the value of model-guided design as an approach for generating useful DNA parts.