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[学术文献 ] Production of a δ-Lactam from Glucose through Integrating Biological and Chemical Catalysis 进入全文
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
We present a new strategy for the production of a δ-lactam from glucose that integrates biological production of triacetic acid lactone (TAL, 4-hydroxy-6-methyl-2H-2-one) with catalytic transformation of TAL into 6-methylpiperidin-2-one (MPO) through metabolic engineering, isomerization, amination, and catalytic hydrogenation/hydrogenolysis. We developed a sustainable and antibiotic-free fed-batch fermentation using genetically modified Rhodotorula toruloides IFO0880. This process achieved a yield of 2-hydroxy-6-methyl-4H-pyran-4-one (2H4P) at 0.05 g/g of glucose, corresponding to a 9.9 g/L titer. By adjusting the pH of the fermentation broth to 2, 2H4P was quantitatively converted into TAL. The TAL in the fermentation broth was directly converted by aminolysis into 4-hydroxy-6-methylpyridin-2(1H)-one (HMPO), which achieved an 18.5% yield with 94.3% purity. The HMPO yield was lower in the fermentation broth than in a clean feedstock (32.2%), suggesting that the biological impurities are inhibitors in this reaction. Further investigation revealed that lower pH levels and reduced TAL concentrations in the fermentation broth significantly decreased HMPO yields. Subsequently, the precipitated HMPO was filtered and dried and then subjected to the final catalytic conversion in H2O solvent, achieving a MPO yield of 91.8%. This integrated approach demonstrated the direct use of TAL in the filtered aqueous fermentation broth without the need to isolate TAL.
[前沿资讯 ] The evolution of low-temperature adapted enzymes 进入全文
EurekAlert
A research team led by Professor Satoshi Akanuma from Waseda University, Japan, in collaboration with Assistant Professor Sota Yagi from Waseda University, Dr. Subrata Dasgupta, and Dr. Shunsuke Tagami from the RIKEN Center for Biosystems Dynamics Research, investigated the evolutionary improvement of IPMDH activity at low temperatures. They traced its evolution from the enzyme of the most ancient thermophilic common ancestor to the mesophilic bacterium Escherichia coli using ASR. Their study was published online in the journal Protein Science on February 19, 2025.“We reconstructed 11 intermediate ancestral enzymes along the evolutionary trajectory connecting the last common bacterial ancestor and E. coli IPMDH (EcIPMDH),” explains Akanuma. “After that, we analyzed changes in enzyme activity at each evolutionary stage, especially improvements in catalytic activity at low temperatures.” They observed a notable increase in catalytic activity at 25 °C, which did not follow a gradual, linear pattern. Instead, a dramatic improvement occurred between the fifth (Anc05) and sixth (Anc06) intermediate ancestors. What caused this sudden boost in enzymatic efficiency? To find the underlying molecular mechanisms, the researchers compared the amino acid sequences of the ancestral enzymes and used site-directed mutagenesis, a technique that allows precise alterations to DNA and protein sequences. They identified three key amino acid substitutions that significantly enhanced catalytic activity at 25 °C. Surprisingly, these mutations occurred far from the active site, challenging the previous belief that temperature adaptation is primarily driven by active-site modifications. Molecular dynamics simulations revealed a key structural shift between Anc05 and Anc06. While Anc05 remained in an open conformation, Anc06 could adopt a partially closed conformation, reducing activation energy and enhancing enzymatic efficiency at low temperatures. This transition occurred 2.5–2.1 billion years ago, coinciding with the Great Oxidation Event, which led to a sharp decline in atmospheric methane and global cooling. The researchers suggest that this climate shift may have driven the adaptation of enzymes to lower temperatures. By identifying key mutations that enhance enzyme efficiency, ASR provides valuable insights into how life evolved in response to Earth's changing environment. “Applying this approach to various enzymes is expected to reveal how organisms and their enzymes have evolved in response to Earth's environmental changes over the past four billion years,” Akanuma concluded.
[学术文献 ] Synthetic Biology in Natural Product Biosynthesis 进入全文
Chemical Reviews
Synthetic biology has played an important role in the renaissance of natural products research during the post-genomics era. The development and integration of new tools have transformed the workflow of natural product discovery and engineering, generating multidisciplinary interest in the field. In this review, we summarize recent developments in natural product biosynthesis from three different aspects. First, advances in bioinformatics, experimental, and analytical tools to identify natural products associated with predicted biosynthetic gene clusters (BGCs) will be covered. This will be followed by an extensive review on the heterologous expression of natural products in bacterial, fungal and plant organisms. The native host-independent paradigm to natural product identification, pathway characterization, and enzyme discovery is where synthetic biology has played the most prominent role. Lastly, strategies to engineer biosynthetic pathways for structural diversification and complexity generation will be discussed, including recent advances in assembly-line megasynthase engineering, precursor-directed structural modification, and combinatorial biosynthesis.
[前沿资讯 ] 中科院天津工业生物技术研究所在大语言模型助力生物制造应用方面取得进展 进入全文
中科院天津工业生物技术研究所
近日，中国科学院天津工业生物技术研究所生物设计中心开发了基于LLMs的SynBioGPT菌种改造专家系统（https://synbiogpt.biodesign.ac.cn）。该系统已通过海外科学家验证，取得了良好的效果。相关研究进一步全面分析了AI大语言模型在合成生物学应用方面的最新进展，深入探讨了利用这些AI大模型推动细胞工厂设计和代谢工程菌种改造的可行路径。SynBioGPT整合了51,777篇文献摘要和23,318篇开放获取全文PDF，测试了LLMs在合成生物学问题上的表现。结合检索增强生成（Retrieval-Augmented Generation,RAG）技术后，LLMs的回答准确性从25%显著提升至85%，其中Qwen1.5和Llama3模型表现尤为突出。为了进一步验证LLMs在生物制造中的应用潜力，团队进一步分析了其在生物序列建模、细胞工厂开发和自驱动实验室（Self-Driving Laboratories,SDL）中可能发挥的作用。首先，LLMs在处理DNA、RNA和蛋白质序列数据中具有独特优势，特别是在蛋白质语言模型中能够生成通用表示，为构建AI虚拟细胞（AI Virtual Cell,AIVC）奠定基础。其次，在细胞工厂开发中，LLMs通过整合文献数据和实验报告，加速了酶工程、途径设计和发酵优化的设计–构建–测试–学习（DBTL）周期，其能够提取关键特征并与代谢模型结合，从而提高机器学习预测能力并优化生物制造效率。最后，作为智能代理，LLMs通过任务规划、实验设计和数据分析推动生物制造向SDL范式转变，SDL结合机器人技术与人类监督，能够实现从任务分解到实验执行的全流程自动化，为未来智能化生产奠定基础。综上所述，该研究详细阐明了LLMs在合成生物学知识合成和生物制造智能化中的应用机制，展示了其在提升生产效率和可持续性方面的潜力。同时，该研究也为LLMs在合成生物学中的应用提供了新的视角，拓展了其在生物催化、药物开发和环保技术中的研究领域。
[前沿资讯 ] Green recipe: Engineered yeast boosts D-lactic acid production 进入全文
EurekAlert
Seeking a more efficient way to produce D-lactic acid, the team turned to Komagataella phaffii, a yeast capable of utilizing methanol. Their goal was to pinpoint the optimal combination of D-lactate dehydrogenase (D-LDH) genes and promoters in K. phaffii that would maximize the yeast’s ability to produce D-lactic acid from methanol. D-LDH is a key enzyme responsible for converting precursor molecules into D-lactic acid, while promoters are DNA sequences that regulate gene expression. After testing five different D-LDH genes and eight promoters, the researchers identified an ideal mix that boosted D-lactic acid production by 1.5 times compared to other methanol-based methods. “To the best of our knowledge, our engineered yeast achieved the highest-ever reported yield using methanol as the sole carbon source,” Yamada said. These findings show that engineered yeast strains can be tailored to produce a wide range of useful compounds for commercial use. With growing global concerns over fossil fuel depletion and environmental impact, the ability to synthesize chemicals from renewable carbon sources like methanol is deemed a critical advancement for sustainability. “This study demonstrates that by carefully optimizing gene and promoter combinations, we can significantly enhance the efficiency of microbial processes, offering a viable alternative to traditional, petroleum-based chemical production,” Yamada said. The study was published in Biotechnology for Biofuels and Bioproducts.
[学术文献 ] Generative artificial intelligence for enzyme design: Recent advances in models and applications 进入全文
CURRENT OPINION IN GREEN AND SUSTAINABLE CHEMISTRY
Enzyme catalysis is a key enabling technology for green and sustainable production of chemicals. Developing suitable enzymes is at the heart of this technology, which is currently changing by Artificial Intelligence (AI) such as machine learning. AI-based methods were used for enzyme discovery and design. We review the recent advances in generative AI models for enzyme design, with a particular focus on those that have been validated by experiments. Furthermore, we discuss the applications of the enzymes designed by generative AI, including artificial luciferases, non-heme iron (II)-dependent oxygenases, and P450 enzymes. We provide our opinions on several current issues encountered in computational enzyme design. With the fast development of new generative models in enzymes and the implementation of these models by the research community, we believe that the precise design of efficient enzymes with new catalytic functions and/or potential industrial applications will be a mature method in the near future.
