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[学术文献 ] A metagenomic ‘dark matter’ enzyme catalyses oxidative cellulose conversion 进入全文
Nature
The breakdown of cellulose is one of the most important reactions in nature1,2 and is central to biomass conversion to fuels and chemicals3. However, the microfibrillar organization of cellulose and its complex interactions with other components of the plant cell wall poses a major challenge for enzymatic conversion4. Here, by mining the metagenomic ‘dark matter’ (unclassified DNA with unknown function) of a microbial community specialized in lignocellulose degradation, we discovered a metalloenzyme that oxidatively cleaves cellulose. This metalloenzyme acts on cellulose through an exo-type mechanism with C1 regioselectivity, resulting exclusively in cellobionic acid as a product. The crystal structure reveals a catalytic copper buried in a compact jelly-roll scaffold that features a flattened cellulose binding site. This metalloenzyme exhibits a homodimeric configuration that enables in situ hydrogen peroxide generation by one subunit while the other is productively interacting with cellulose. The secretome of an engineered strain of the fungus Trichoderma reesei expressing this metalloenzyme boosted the glucose release from pretreated lignocellulosic biomass under industrially relevant conditions, demonstrating its biotechnological potential. This discovery modifies the current understanding of bacterial redox enzymatic systems devoted to overcoming biomass recalcitrance5,6,7. Furthermore, it enables the conversion of agro-industrial residues into value-added bioproducts, thereby contributing to the transition to a sustainable and bio-based economy.
[学术文献 ] Rational multienzyme architecture design with iMARS 进入全文
Cell
Biocatalytic cascades with spatial proximity can orchestrate multistep pathways to form metabolic highways, which enhance the overall catalytic efficiency. However, the effect of spatial organization on catalytic activity is poorly understood, and multienzyme architectural engineering with predictable performance remains unrealized. Here, we developed a standardized framework, called iMARS, to rapidly design the optimal multienzyme architecture by integrating high-throughput activity tests and structural analysis. The approach showed potential for industrial-scale applications, with artificial fusion enzymes designed by iMARS significantly improving the production of resveratrol by 45.1-fold and raspberry ketone by 11.3-fold in vivo, as well as enhancing ergothioneine synthesis in fed-batch fermentation. In addition, iMARS greatly enhanced the in vitro catalytic efficiency of the multienzyme complexes for PET plastic depolymerization and vanillin biosynthesis. As a generalizable and flexible strategy at molecular level, iMARS could greatly facilitate green chemistry, synthetic biology, and biomanufacturing.
[前沿资讯 ] 新型工程酶变体可拓展环亚胺酸结构多样性 进入全文
科学网
中国科学院上海药物研究所研究员廖苍松课题组与中国科学院天津工业生物技术研究所研究员盛翔课题组合作，利用聚焦理性迭代位点特异性突变（FRISM）策略，对脱羧醛缩酶UstD进行了半理性工程改造，调控了UstD对邻位二酮亲电试剂的区域选择性和立体选择性，获得的工程酶变体具有极佳的选择性和较广的底物谱，拓展了环亚胺酸的结构多样性。1月15日，相关研究在线发表于《德国应用化学》。研究团队使用FRISM的半理性策略开展酶工程改造研究，通过酶变体设计探索底物结合口袋的残基对选择性的调控机制。经过三轮突变后得到的UstD2.0AAM对产物1c的转化率为46%，选择性为90%，对产物2b的转化率为72%，选择性为94%。研究人员解析了2b的立体构型，并高选择性地合成了共计30种在α和γ位置具有立体中心的环状亚胺酸，实现了百毫克规模的制备反应，收率达89%。分子动力学模拟研究表明，ApUstD和UstD2.0AAM两种酶活性位点空腔大小和疏水性存在明显差异，这些差异导致了底物在口袋内具有不同的结合构象，也是不同酶表现出不同反应选择性的根本原因。
[学术文献 ] Multi-Modal CLIP-Informed Protein Editing 进入全文
Health Data Science
Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. Methods: To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises 2 stages: In the pretraining stage, contrastive learning aligns protein–biotext representations encoded by 2 large language models (LLMs). Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. Results: Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability, and antibody-specific binding ability. ProtET improves the state-of-the-art results by a large margin, leading to substantial stability improvements of 16.67% and 16.90%. Conclusions: This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs.
[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。
[前沿资讯 ] 天津工业生物技术研究所实现大肠杆菌实时动态调控葡萄糖摄取率及中心途径代谢 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张大伟研究员带领的蛋白表达系统与微生物代谢研究团队开发了实时动态监测大肠杆菌葡萄糖吸收速率的方法及其遗传回路，能够动态调节葡萄糖摄取速率及相关代谢途径的碳通量。在大肠杆菌摄取葡萄糖时，会经历一系列复杂的过程，包括跨膜转运、磷酸化、去磷酸化、辅助蛋白招募，以及相关因子的表达或抑制等。基于此调控机制，研究团队开发出了能够实时响应葡萄糖摄取速率的生物传感器（GURBs）（图1），并建立了对葡萄糖摄取速率和中央代谢流进行正负调节的遗传回路。GURBs的性能和灵敏度在不同条件下得到了验证。在线荧光和离线葡萄糖检测技术表明，GURBs可以直接测量葡萄糖摄取速率。GURBs被应用于氨基酸，维生素，有机酸等产品合成（图2），通过调控中央代谢途径代谢流，或调控遗传回路的激活或抑制，有效的提高了其产量。这些结果表明，GURBs可以根据葡萄糖摄取速率动态调节葡萄糖摄取率，及中央代谢和相关途径的碳通量，从而提高目标产品产量。葡萄糖作为细胞摄取碳源的第一步，建立其实时监测及动态调控技术十分重要，通过基因回路优化代谢流分配，不仅能很好地适应培养环境变化，还能有效平衡细胞生长与产物合成之间的代谢竞争，合理分配和利用碳资源，为合成生物设计与细胞工厂的构建提供了重要工具和更多选择。
