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

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We developed STORM and NuSpeak, deep learning models that predict and optimize toehold switch performance based on sequence. This accelerates the design of novel biological sensors and circuits.

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Area of Science:

  • Synthetic Biology
  • Molecular Biology
  • Computational Biology

Background:

  • Designing novel biological circuit components is challenging due to limited understanding of sequence-function relationships.
  • Toehold switches, programmable nucleic acid sensors, require extensive screening to identify effective designs.

Purpose of the Study:

  • To introduce deep learning frameworks for characterizing and optimizing toehold switches.
  • To enable rapid, sequence-based design of functional toehold sensors.

Main Methods:

  • Developed Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak) deep learning architectures.
  • Applied computer vision and natural language processing techniques, including convolutional filters and attention maps.
  • Utilized in silico mutagenesis and transfer learning for model refinement and redesign.

Main Results:

  • Demonstrated ability to characterize and optimize toehold switches using sequence data.
  • Successfully redesigned sub-optimal toehold sensors with improved experimental performance, even with limited training data.
  • Validated the effectiveness of deep learning frameworks for predicting toehold functionality.

Conclusions:

  • STORM and NuSpeak provide powerful sequence-to-function frameworks for toehold selection and design.
  • These models accelerate the development of potent biological circuit components and precision diagnostics.
  • Advances synthetic biology design capabilities by overcoming traditional screening bottlenecks.