Context-Aware Synthetic Promoter Design Using Neural Networks Enables Rewiring of Eukaryotic Transcriptional Networks

Gene regulation through promoter engineering is a cornerstone of synthetic biology, enabling precise control over transcriptional networks. However, experimental approaches remain labor-intensive. While artificial neural networks (ANNs) have improved regulatory element prediction, tools for promoter–transcription factor binding site (TFBS) recombination are still lacking. We present an ANN framework for context-aware design of synthetic promoters in Saccharomyces cerevisiae. The model predicts optimal TFBS insertion sites and the extent of promoter rewriting needed for successful integration. Applying this, we screened 6,011 native yeast promoters for compatibility with the TetR TFBS, generating a ranked list of high-confidence promoter–TFBS pairs. Experimental validation showed that model-designed promoters achieved repression rates up to 98.4%, without prior experimental characterization or tuning. We further rewired the yeast transcriptional network by introducing glucose-dependent regulation of an essential gene via Mig1 TFBS insertion. These results establish a scalable, predictive method for engineering regulatory sequences and reprogramming transcriptional logic.

通过启动子工程实现基因调控是合成生物学的核心支柱，可实现对转录网络的精准调控。不过，传统实验方法仍存在劳动强度高、耗时费力的局限。尽管人工神经网络（Artificial Neural Networks, ANNs）已在调控元件预测领域取得显著进展，但针对启动子-转录因子结合位点（Transcription Factor Binding Site, TFBS）重组的专用工具仍较为匮乏。本研究提出一种面向酿酒酵母（Saccharomyces cerevisiae）合成启动子的上下文感知设计人工神经网络框架。该模型可精准预测最优的TFBS插入位点，以及成功整合所需的启动子重写程度。基于该框架，我们针对6011个酿酒酵母天然启动子开展了与TetR转录因子结合位点兼容性的筛选工作，最终生成了高置信度启动子-TFBS对的排序列表。实验验证结果显示，经模型设计的启动子可实现最高达98.4%的基因抑制效率，且无需预先开展实验表征或参数调优。我们进一步通过插入Mig1转录因子结合位点，实现了必需基因的葡萄糖依赖型调控，从而完成了酿酒酵母转录网络的重布线。上述研究成果构建了一种可扩展的预测性方法，可用于调控序列的工程化改造与转录逻辑重编程。