Sequence-to-function deep learning frameworks for engineered riboregulators

Jacqueline A. Valeri, Katherine M. Collins, Pradeep Ramesh, Miguel A. Alcantar, Bianca A. Lepe, Timothy K. Lu () and Diogo M. Camacho ()
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Jacqueline A. Valeri: Harvard University
Katherine M. Collins: Harvard University
Pradeep Ramesh: Harvard University
Miguel A. Alcantar: Massachusetts Institute of Technology
Bianca A. Lepe: Harvard University
Timothy K. Lu: Massachusetts Institute of Technology
Diogo M. Camacho: Harvard University

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18676-2

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