Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Abstract

23Currently, predictive translation tuning of regulatory elements to the desired output of 24 transcription factor based biosensors remains a challenge. The gene expression of a biosensor 25 system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding 26 site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between 27 the presence and absence of inducer) by adjusting the translation initiation rate of the transcription 28 factor and reporter. However, existing genetically encoded biosensors generally suffer from 29 unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the 30 connections and partial mechanisms between RBS, translation initiation rate, protein folding and 31 dynamic range, and presented a rational design platform that predictably tuned the dynamic range 32 of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 33 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided 34into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship 35 between cRBSs and dynamic range, we established a classification model with the cRBSs and 36 average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors 37 based on convolutional neural network in deep learning. Thus, this work provides a powerful 38 platform to enable predictable translation tuning of RBS to the dynamic range of biosensors. 39 40

Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917

“…The coding sequences of regulators have also been mutated to improve sensor performance, such as by altering their ligand binding affinity (Lee et al, 2007;McCready et al, 2019;Meyer et al, 2019). Lastly, another common strategy that has been used to tune sensors is to optimize the expression of the regulator by changing the promoter or ribosome binding site (RBS) controlling it (Brophy and Voigt 2014;Daeffler et al, 2017;Xiao et al, 2017;Meyer et al, 2019;Wang et al, 2019;Ding et al, 2020). However, as the DNA elements of a sensor are altered, there are often tradeoffs in the sensor's attributes and many properties affected simultaneously, such as leakiness, dynamic range, maximum promoter strength, sensitivity, specificity, and toxicity.…”

Signal amplification and optimization of riboswitch-based hybrid inputs by modular and titratable toehold switches

“…Analogous to other biosensors, the fold-change is key parameter of riboswitch performance [15,16]. The foldchange refers to the ratio of the minimum and maximum output signals.…”