National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, People's Republic of China.
Nucleic Acids Res. 2020 Oct 9;48(18):10602-10613. doi: 10.1093/nar/gkaa786.
Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-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 level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.
目前，将调控元件的预测性翻译调谐到基于转录因子 (TF) 的生物传感器的预期输出仍然是一个挑战。生物传感器系统的基因表达必须表现出适当的翻译强度，这由核糖体结合位点 (RBS) 控制，通过调整 TF 和报告基因的翻译水平来实现其动态范围的精细调谐（即在诱导物存在和不存在时基因表达的倍数变化）。然而，现有的基于 TF 的生物传感器通常具有不可预测的动态范围。在这里，我们阐明了 RBS、翻译水平、蛋白质折叠和动态范围之间的联系和部分机制，并提出了一个设计平台，该平台可以基于跨 RBS 的大数据集的深度学习来预测性地调整生物传感器的动态范围 (cRBSs)。为此，通过荧光激活细胞分选将包含 7053 个设计的 cRBS 的文库分为五个子库，在深度学习中建立基于卷积神经网络的分类模型。最后，本工作展示了一个强大的平台，能够实现对生物传感器的 RBS 进行可预测的翻译调谐。
