整合深度学习和合成生物学:通过N-终端编码序列增强基因表达的联合设计方法
Zhanglu Yan1, Weiran Chu2, Yuhua Sheng2
1School of Computing, National University of Singapore, Singapore 117417, Singapore.
ACS synthetic biology
|September 4, 2024
概括
优化N端编码序列 (NCS) 增强了基因工程的基因表达. 一个新的深度学习工作流实现了蛋白质生产的5.41倍增加,大大改善了现有方法.
科学领域:
- 合成生物学 合成生物学
- 计算生物学 计算生物学
- 基因工程是一种基因工程.
背景情况:
- N-终端编码序列 (NCS) 通过控制翻译启动率来调节基因表达,这是基因工程中的关键因素.
- 现有的NCS优化方法,包括理性设计和统计方法,通常是劳动密集型的,并产生有限的改进.
- 开发高效的NCS优化策略对于最大化基因表达和推进合成生物学应用至关重要.
研究的目的:
- 引入一种新的深度学习和合成生物学代码设计工作流程,以实现高效的N端编码序列优化.
- 开发一种能够用有限的培训数据识别最佳NCS的方法,克服当前方法的局限性.
- 为了证明优化的NCS在增强蛋白质生产和代谢工程方面的实际实用性.
主要方法:
- 采用了几次的学习工作流程,使用k-nearest编码和word2vec进行NCS表示.
- 使用注意力机制来提取特征,然后使用时间序列网络来预测基因表达.
- 一个直接搜索算法确定了最佳的NCS,其中Bacillus subtilis中的绿色光蛋白 (GFP) 用作报告系统.
主要成果:
- 开发的模型生成了一个优化的NCS (MLD62),在六次实验代中实现了平均GFP表达的5.41倍增加.
- 在光增强因子方面,MLD62 NCS显著超过了最先进的NCS设计.
- 经过工程设计的MLD62 NCS通过促进GNA1基因的表达,成功地增强了N-乙神经胺酸的产生.
结论:
- 基于深度学习的工作流提供了一个高效和有效的NCS优化方法,显著改善基因表达.
- MLD62 NCS证明了其广泛的适用性和实用的实用性超出了记者蛋白,包括在代谢工程中.
- 该研究的发现和资源,包括NCS表达式数据库,是开源的,以促进进一步的研究和开发.
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