Sequence-to-function deep learning frameworks for engineered riboregulators
Name
s41467-020-18676-2.pdf
Description
Published version
Size
1.97 MB
Format
Adobe PDF
Checksum (MD5)
6d97c644f53aedbbe40e72bb5ac1e853
Author(s)
Valeri, Jacqueline A
Collins, Katherine M
Ramesh, Pradeep
Alcantar, Miguel A
Lepe, Bianca A
Lu, Timothy K
Camacho, Diogo M
Date Issued
2020
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). 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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.1038/s41467-020-18676-2
https://doi.org/10.1038/s41467-020-18676-2
