Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1). BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-kcat (TUST-kcat) module predicts the kcat values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

中文翻译：

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基于 AI 的高精度 Geobacillus thermoglucosidasius 酶约束模型的自动化构建


Geobacillus thermoglucosidasius NCIMB 11955 具有耐高温、生长速度快、污染风险低等优点。此外，它还具有高效的基因编辑工具，使其成为最有前途的下一代细胞工厂之一。然而，作为一种非模型微生物，缺乏代谢信息严重阻碍了高精度代谢通量模型的构建。在这里，我们提出了一种基于人工智能技术的 BioIntelliModel （BIM） 策略，用于自动构建酶约束模型。1）. BIM 利用对比学习启用的酶注释 （CLEAN） 预测工具来分析 G. thermoglucosidasius NCIMB 11955 的整个基因组序列，揭示非模型菌株中的潜在功能蛋白。2）. BIM 的 MetaPatchM 模块可自动修复代谢网络模型。3）. 天津科技大学-kcat （TUST-kcat） 模块预测模型内酶的 kcat 值。4）. Enzyme-insert 程序构建一个酶约束模型并执行全局扫描以解决过度约束问题。酶数据自动整合到代谢通量模型中，创建一个酶约束模型 ec_G-ther11955。为了验证模型的准确性，我们使用 p-thermo 和 ec_G-ther11955 模型来预测核黄素生产策略。ec_G-ther11955 模型表现出明显更高的准确性。为了进一步验证其疗效，我们采用 ec_G-ther11955 指导产 L-缬氨酸菌株的合理设计。 使用识别导致靶向过生产的所有遗传操作的优化程序 （OptForce）、预测敲除靶向靶向 （PKT） 和基于强制客观通量 （FSEOF） 算法的通量扫描，我们确定了 24 个敲除和过表达靶点，准确率为 87.5%。最终，这导致 L-缬氨酸滴度增加了 664.04%。本研究为快速构建非模型应变模型提供了一种新策略，并展示了人工智能在代谢工程中的巨大潜力。