arXiv 2402.132972024-02-20q-bio.QMcs.AI

整合深度学习与合成生物学：一种通过N端编码序列增强基因表达的协同设计方法

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-terminal Coding Sequences

School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Science Center for Future Foods, Jiangnan University（江南大学未来食品科学中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）

Zhanglu Yan, Weiran Chu, Yuhua Sheng, Kaiwen Tang, Shida Wang, Yanfeng Liu, Weng-Fai Wong

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AI总结：

本文提出深度学习与合成生物学协同设计的少样本NCS优化方法，结合k近邻编码、word2vec、注意力机制和时间序列网络，仅经六轮迭代即获得使GFP表达提高5.41倍的NCS（MLD62），并验证其可提升N-乙酰神经氨酸产量。

摘要：

N端编码序列（NCS）通过影响翻译起始速率来调控基因表达。NCS优化问题旨在寻找能够最大化基因表达的NCS。该问题在基因工程中具有重要意义。然而，当前用于NCS优化的方法，如理性设计和统计引导方法，劳动强度大且仅能带来相对较小的改进。本文介绍了一种深度学习/合成生物学协同设计的少样本训练工作流程，用于NCS优化。我们的方法利用k近邻编码结合word2vec对NCS进行编码，然后使用注意力机制进行特征提取，接着构建时间序列网络以预测基因表达强度，最后通过直接搜索算法在有限训练数据下识别最优NCS。我们以枯草芽孢杆菌表达的绿色荧光蛋白（GFP）作为NCS的报告蛋白，并采用荧光增强因子作为NCS优化的度量指标。在仅六轮迭代实验中，我们的模型生成了一个NCS（MLD62），使平均GFP表达量提高了5.41倍，优于当前最先进的NCS设计。将我们的发现扩展到GFP之外，我们证明了所设计的NCS（MLD62）能够通过增强关键限速基因GNA1的表达来有效提高N-乙酰神经氨酸的产量，展示了其实际应用价值。我们已开源NCS表达数据库和实验流程，供公众使用。

英文摘要：

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology co-designed few-shot training workflow for NCS optimization. Our method utilizes k-nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD62) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.

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