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[科技报告 ] OECD Synthetic biology in focus: Policy issues and opportunities in engineering life 进入全文
OECD
Synthetic biology promises to revolutionise a swath of industrial activities and create new ones by tailoring living systems to produce a range of products to boost economies, transform health and contribute to solving grand societal challenges. In 2023 and 2024, over sixty experts from around the globe came together regularly to explore where synthetic biology will have the most impact, identify the challenges and opportunities in developing and deploying synthetic biology around the world, and to explore areas where policy could help. This working paper provides a synthesis of this scoping activity, providing an accessible text for those new to the rapidly evolving area of synthetic biology.
[科技报告 ] The Stanford Emerging Technology Review 2025 (Biotechnology and Synthetic Biology) 进入全文
Stanford University
KEY TAKEAWAYS • Biotechnology is poised to emerge as a general-purpose technology by which anything bioengineers learn to encode in DNA can be grown whenever and wherever needed— essentially enabling the production of a wide range of products through biological processes across multiple sectors. • The US government is still working to grasp the scale of this bio-opportunity and has relied too heavily on private-sector investment to support the foundational technology innovation needed to unlock and sustain progress. • Biotechnology is one of the most important areas of technological competition between the United States and China, and China is investing considerably more resources. Lacking equivalent efforts domestically, the United States runs the risk of Sputnik-like strategic surprises in biotechnology. Overview Biotechnology partners with biology to create products and services, like engineering skin microbes to fight cancer or brewing medicines from yeast. This industry, already 5 percent of US GDP, is poised for significant growth. Synthetic biology, a subset of biotechnology focusing on enhancing living systems, relies on DNA sequencing and synthesis. DNA sequencers are machines that read or decode specific DNA molecules, while synthesizers write user-specifi ed sequences of DNA. Rapid progress in these technologies is driving innovation and expanding biotechnology’s potential applications. Biology as a manufacturing process is distributed; leaves do not come from a central production facility but rather grow on trees everywhere. However, commercial biotechnology has become centralized and capital intensive. This contrast suggests a potential paradigm shift toward a more distributed approach in biotechnology, aligning it more closely with nature’s decentralized production model. Synthetic biology merges biology, engineering, and computer science to modify and create living systems, developing novel biological functions served by amino acids, proteins, and cells not found in nature. This fi eld creates reusable biological “parts,” streamlining design processes and reducing the need to start from scratch, thus advancing biotechnology’s capabilities and efficiency. Synthetic biology has applications in medicine, agriculture, manufacturing, and sustainability. DNA and RNA synthesis underlies all mRNA vaccines, including those for COVID-19. Synthetic biology can also cultivate drought-resistant crops and enable cells to be programmed to manufacture medicines or fuel on an agile, distributed basis. Key Developments Distributed biomanufacturing This offers unprecedented production flexibility in both location and timing. Fermentation production sites can be established anywhere with access to sugar and electricity. This approach enables swift responses to sudden demands like disease outbreaks requiring specific medications. Such adaptability revolutionizes manufacturing, making it more efficient and responsive to urgent needs. Biology as a general-purpose technology Currently, biotechnology is used to make medicines, foods, and a narrow range of sustainable materials. But anything whose synthesis can be encoded in DNA could be grown. For example, some bacteria are capable of growing arrays of tiny magnets, and select sea sponges grow glass filaments similar to human-made fiber-optic cables. These and other examples suggest the potential for biology to be recognized as a general-purpose technology that could become the foundation of a more resilient manufacturing base. Biological large language models (BioLLMs) Large language models (LLMs), which are a form of artificial intelligence, have emerged that are being trained on natural DNA, RNA, and protein sequences. Called BioLLMs, they can generate new biologically significant sequences that are helpful points of departure for designing useful proteins.
[学术文献 ] Integrating protein language models and automatic biofoundry for enhanced protein evolution 进入全文
Nature Communications
Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.
[学术文献 ] Semirationally Engineering an Efficient P450 Peroxygenase for Regio- and Enantioselective Hydroxylation of Steroids 进入全文
ACS Catalysis
Enzymatic direct hydroxylation of unactivated C–H bonds in steroids provides a promising approach to enrich their structural and functional diversity, together with higher physiological and pharmacological activity. Here, we construct an efficient peroxide-driven P450 hydroxylase for the regio- and enantioselective hydroxylation of steroids. The NADH-dependent CYP154C5 monooxygenase is smoothly transformed into its peroxygenase mode by combining the strategies of H2O2 tunnel engineering and the introduction of a catalytic aspartate residue, which avoids the use of expensive nicotinamide cofactors and redox partner proteins. The variant F92A/R114A/E282A/T248D (AAA/T248D) quantitatively converted testosterone and nandrolone into the corresponding 16α-hydroxylation products, showing the best catalytic efficiency (kcat/Km) for testosterone hydroxylation among all known natural and engineered P450 peroxygenases to date. Crystal structural analysis and molecular dynamics simulations suggest that H2O2 tunnel engineering plays a crucial role in promoting the flow of H2O2 into active centers, and the introduced aspartate residue may participate in the activation of H2O2. Moreover, the milligram-scale preparation of 16α-hydroxytestosterone by AAA/T248D gave a substrate conversion rate (>98%) and an isolated yield (90%), suggesting potential for synthetic application. This work not only establishes a feasible semirational approach to engineered non-natural P450 peroxygenases but also provides a potentially practical approach for the enzymatic synthesis of hydroxylated steroid compounds.
[学术文献 ] Patulin-degrading enzymes sources, structures, and mechanisms: A review 进入全文
International Journal of Biological Macromolecules
Patulin (PAT), a fungal secondary metabolite with multiple toxicities, is an unavoidable contaminant in fruit and vegetable processing, posing potential health risks to consumers and causing significant economic losses to the global food industry. Traditional control strategies, such as physical and chemical methods, face several challenges, including low efficiency, high costs, and unverified safety. In contrast, microbial degradation of patulin is considered a more efficient and environmentally friendly approach, which has become a popular research focus. However, there is still insufficient research on the key degradation enzymes involved in microorganisms. Therefore, this review comprehensively summarizes recent research progress on the biological degradation of patulin, with a focus on microbial species capable of degrading patulin, the degradation enzymes they express, potential degradation mechanisms, and the toxicity of degradation products, while providing prospects for future research. It offers valuable insights for controlling patulin in food and stimulates further investigation. Ultimately, this review aims to promote the development of efficient and eco-friendly methods to mitigate patulin contamination in fruits and vegetables.
[前沿资讯 ] 中国科学院深圳先进技术研究院合成生物学研究所研究基于对比学习的酶促反应分类AI模型 进入全文
中国科学院深圳先进技术研究院
中国科学院深圳先进技术研究院的罗小舟领衔的研究团队，近日在Journal of Cheminformatics期刊发表重要研究成果"CLAIRE: A Contrastive Learning-based Predictor for EC Number of Chemical Reactions"。在该研究成果中，团队利用对比学习，数据扩增，以及基于化学反应预训练模型的特征提取（embedding）策略，构建了一个用于预测EC分类编号的高效人工智能模型（CLAIRE）。作者将CLAIRE与当前最领先的Theia模型进行了对比。Theia是2023年由瑞士洛桑联邦理工学院的科学家Daniel Probst发表在Journal of Cheminformatics期刊上的基于常规深度学习的模型——然而常规深度学习方法不能有效解决数据不平衡的问题。借助对比学习和数据扩增的策略，CLAIRE展现出了优异的性能——在测试集上，CLAIRE比Theia有数倍的准确率提升，且在三级EC分类编号预测之间的一致性也显著高于Theia。此外，作者利用酵母菌的代谢模型构建了另一个大型独立测试集。在该数据集中，CLAIRE的表现也显著高于Theia。 通过一系列严格的评估，研究人员展示了CLAIRE的强大能力：在酵母代谢模型中，它成功区分了真实的酶-反应配对与错误配对。代谢模型是生物体内代谢反应的定量化表示，涵盖基因、酶、代谢物及其细胞内分布，广泛应用于代谢工程和通量平衡分析等领域。CLAIRE的加入使得研究人员能够更高效地分析和注释反应网络，为代谢研究提供了全新可能。此外，CLAIRE在逆合成路径规划和药物代谢预测等关键领域展示出巨大应用潜力。逆合成预测旨在推断生成目标化合物所需的原料及反应路径。在这一过程中，多个中间产物可能生成大量候选反应。通过CLAIRE预测的EC编号，可为这些反应分配相关酶，大幅提升最终目标化合物成功合成的可能性。另外，药物在人体内的代谢转化及路径是评估其安全性和有效性的重要环节。通过对潜在反应注释EC编号，CLAIRE能够清晰描绘可能的药物代谢路径，为毒性评估及药物开发提供有力支持。总而言之，该项成果在代谢工程和合成生物学领域中有着广泛的应用。
