Sequence-to-function deep learning frameworks for engineered riboregulators

Nat Commun. 2020 Oct 7;11(1):5058. doi: 10.1038/s41467-020-18676-2.


While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Sequence / genetics
  • Biotechnology / methods*
  • Computer Simulation
  • Datasets as Topic
  • Deep Learning*
  • Genetic Engineering / methods*
  • Genome, Human / genetics
  • Genome, Viral / genetics
  • Humans
  • Models, Genetic
  • Mutagenesis
  • Natural Language Processing
  • Riboswitch / genetics*
  • Structure-Activity Relationship
  • Synthetic Biology / methods*


  • Riboswitch