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微生物学和机器学习辅助方法揭示了高温Daqu中的氨基酸模式

Chao Fang¹, Gailing Shi¹, Qing He¹

¹Engineering Research Center of Food Biotechnology, Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, State Key Laboratory of Food Nutrition and Safety, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, People's Republic of China.

|July 11, 2025

概括

这项研究使用机器学习确定了影响高温Daqu (HTD) 中氨基酸概况的关键微生物. 这些发现为预测和控制发酵食品中的代谢物水平提供了一个新的策略.

科学领域:

食品微生物学食品微生物学
发酵科学发酵科学
代谢学代谢学代谢学

背景情况:

氨基酸是高温达基 (HTD) 中重要的源,影响其整体质量.
了解氨基酸动态对于优化HTD发酵至关重要.

研究的目的:

通过分析发酵参数和微生物群落来研究HTD中的氨基酸模式.
识别氨基酸生产中的关键微生物参与者及其代谢功能.
开发HTD中氨基酸度的预测模型.

主要方法:

相关性分析和机器学习确定了来自Saccharopolyspora,Bacillus,Lactobacillus和Virgibacillus的6种关键安普利康序列变体 (ASV).
功能预测评估了在氨基酸代谢途径中识别的ASVs的酶活性.
开发和验证了集成机器学习模型,用于预测氨基酸度.

主要成果:

来自Saccharopolyspora,Bacillus,Lactobacillus和Virgibacillus的六个关键ASV被确定为氨基酸代谢的重要贡献者.
这些ASV在关键发酵阶段显示出氨基酸通路中的高酶活性.
机器学习模型准确地预测了大多数氨基酸度 (R2 0.700.95),通过独立数据集进行验证.

结论:

这项研究在HTD中建立了特定微生物ASV和氨基酸概况之间的联系.
该研究提供了一种有效的策略,用于预测和调节传统发酵食品中的代谢物度.
这些发现有助于通过微生物和代谢洞察提高HTD的质量和一致性.

关键词:

氨基酸是氨基酸的组成部分.高温的达库高温的达库机器学习是机器学习.微生物社区的微生物社区.预测模型的预测模型.

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