Sequence-to-function deep learning frameworks for synthetic biology

Abstract

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of novel circuit components beyond nature-inspired templates can prove itself challenging without well-established design rules. Toehold switches -- programmable nucleic acid sensors -- face an analogous prediction and design bottleneck: our limited understanding of how sequence impacts functionality can require expensive, time-consuming screens for effective switches. Here, we introduce the Sequence-based Toehold Optimization and Redesign Model (STORM), a deep learning architecture that applies gradient ascent to re-engineer poorly-performing toeholds. Based on a dataset of 91,534 toehold switches, we examined convolutional filters and saliency maps of sequences to interpret our sequence-to-function model, identifying hot spots where mutations change toehold effectiveness and features unique to high-performing switches. Our modeling platform provides frameworks for future toehold selection, augmenting our ability to construct potent synthetic circuit components and precision diagnostics, and enabling straightforward translation of this in silico workflow to other circuitries.