PT  - JOURNAL ARTICLE
AU  - Nana Ding
AU  - Shenghu Zhou
AU  - Zhenqi Yuan
AU  - Xiaojuan Zhang
AU  - Jing Chen
AU  - Yu Deng
TI  - Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria
AID  - 10.1101/2020.01.27.922302
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.27.922302
4099  - http://biorxiv.org/content/early/2020/01/28/2020.01.27.922302.short
4100  - http://biorxiv.org/content/early/2020/01/28/2020.01.27.922302.full
AB  - Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between the presence and absence of inducer) by adjusting the translation initiation rate of the transcription factor and reporter. However, existing genetically encoded biosensors generally suffer from unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation initiation rate, protein folding and dynamic range, and presented a rational design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship between cRBSs and dynamic range, we established a classification model with the cRBSs and average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors based on convolutional neural network in deep learning. Thus, this work provides a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.