Core Viewpoint - The article emphasizes the transformative potential of synthetic biology in addressing sustainability challenges in traditional agricultural protein production, particularly through the development of microbial protein from non-grain raw materials [1][2]. Group 1: Technological Innovations - The research team has made significant breakthroughs in the use of methanol as a renewable C1 compound, enhancing the efficiency of converting methanol into single-cell protein through metabolic engineering and genomic perturbation [2]. - Innovations in gas-phase non-grain microbial protein production include the construction of a light-dark energy adapter in E. coli, enabling light-driven CO2 assimilation [2]. - The team has developed a machine learning model to decode the composition of degrading enzyme systems based on the structural characteristics of lignocellulose, facilitating the production of microbial protein from agricultural waste [2]. Group 2: Economic and Environmental Impact - The innovative conversion models not only enhance the economic value of agricultural waste but also pave the way for the industrial-scale biosynthesis of microbial protein from these resources, achieving a dual benefit of resource utilization [2]. - The research highlights the integration of synthetic biology and interdisciplinary innovation as a driving force for sustainable protein production, contributing to food security and carbon neutrality goals [6]. Group 3: Future Research Directions - Future research will focus on the super-evolutionary design of chassis cells, the closed-loop integration of negative carbon manufacturing systems, and the construction of AI-driven intelligent bio-manufacturing platforms [6].

团队前期的工作围绕甲醇作为一种可再生的 C1 化合物以及与二氧化碳氢化合成技术的重大突破，通过碳氮协同耦合代谢工程与基因组扰动等多重策 略，有效提升天然甲基营养菌中甲醇向单细胞蛋白的定向转化效率，进而突破工业菌株性能极限，为利用甲醇作为碳源生物制造微生物蛋白大规模工业 化生产提供了关键技术支持；在气态非粮生物制造微生物蛋白方面，通过构建大肠杆菌中的光 - 暗反应能量适配器，实现光驱动 CO 2 同化的全细胞 催化过程；通过电催化 - 生物耦合技术，可将电化学还原 CO 2 生成的甲酸盐与微生物同化模块精准对接，为气态非粮原料的转化技术开辟了负碳生 物制造微生物蛋白的新维度；在固态非粮原料生物合成微生物蛋白技术方面，该团队通过机器学习模型，基于木质纤维素结构特性破译出降解酶系组成 的新算法，进而摆脱复杂性底物结构 - 多样性酶系构效关系的实验先验，精准定制了多种地源性木质纤维素来源的微生物蛋白，达到地源性农业废弃 物资源利用与微生物蛋白生物合成"一草双收"效果。这些创新转化模式不仅提升了农业废弃物资源化利用经济价值，更开辟了农业废弃物规模化生物 合成微生物蛋白的工业化新路径。 以下文章来源于中国科学院天津工业生 ...