Sequence-to-function deep learning frameworks for engineered riboregulators.

Journal: Nature communications
PMID: 33028819

Abstract

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.

Authors

  • Jacqueline A Valeri
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
  • Katherine M Collins
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Pradeep Ramesh
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
  • Miguel A Alcantar
    Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Bianca A Lepe
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
  • Timothy K Lu
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.
  • Diogo M Camacho
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.

Keywords

External Resources

Popular Topics
Recent Journals