Zhu Jiawei, Meng Yaru, Gao Wenli, Yang Shuo, Zhu Wenjie, Ji Xiangyang, Zhai Xuanpei, Liu Wan-Qiu, Luo Yuan, Ling Shengjie, Li Jian, Liu Yifan
School of Physical Science and Technology, ShanghaiTech University, Shanghai, China.
State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Nat Commun. 2025 Mar 19;16(1):2720. doi: 10.1038/s41467-025-58139-0.
Cell-free gene expression (CFE) systems enable transcription and translation using crude cellular extracts, offering a versatile platform for synthetic biology by eliminating the need to maintain living cells. However, Such systems are constrained by cumbersome composition, high costs, and limited yields due to numerous additional components required to maintain biocatalytic efficiency. Here, we introduce DropAI, a droplet-based, AI-driven screening strategy designed to optimize CFE systems with high throughput and economic efficiency. DropAI employs microfluidics to generate picoliter reactors and utilizes a fluorescent color-coding system to address and screen massive chemical combinations. The in-droplet screening is complemented by in silico optimization, where experimental results train a machine-learning model to estimate the contribution of the components and predict high-yield combinations. By applying DropAI, we significantly simplified the composition of an Escherichia coli-based CFE system, achieving a fourfold reduction in the unit cost of expressed superfolder green fluorescent protein (sfGFP). This optimized formulation was further validated across 12 different proteins. Notably, the established E. coli model is successfully adapted to a Bacillus subtilis-based system through transfer learning, leading to doubled yield through prediction. Beyond CFE, DropAI offers a high-throughput and scalable solution for combinatorial screening and optimization of biochemical systems.
无细胞基因表达（CFE）系统利用粗细胞提取物实现转录和翻译，通过消除维持活细胞的需求，为合成生物学提供了一个通用平台。然而，由于维持生物催化效率需要大量额外成分，此类系统受到组成繁琐、成本高和产量有限的限制。在此，我们介绍DropAI，一种基于液滴的、由人工智能驱动的筛选策略，旨在以高通量和经济效率优化CFE系统。DropAI采用微流控技术生成皮升反应器，并利用荧光颜色编码系统对大量化学组合进行寻址和筛选。液滴内筛选辅以计算机模拟优化，实验结果训练机器学习模型以估计各成分的贡献并预测高产组合。通过应用DropAI，我们显著简化了基于大肠杆菌的CFE系统的组成，使表达的超级折叠绿色荧光蛋白（sfGFP）的单位成本降低了四倍。这种优化配方在12种不同蛋白质上进一步得到验证。值得注意的是，通过迁移学习，已建立的大肠杆菌模型成功应用于基于枯草芽孢杆菌的系统，通过预测使产量提高了一倍。除了CFE，DropAI为生化系统的组合筛选和优化提供了一种高通量且可扩展的解决方案。
