共检索到176条,权限内显示50条;
[学术文献 ] De novo design of porphyrin-containing proteins as efficient and stereoselective catalysts 进入全文
Science
De novo design of protein catalysts with high efficiency and stereoselectivity provides an attractive approach toward the design of environmentally benign catalysts. Here, we design proteins that incorporate histidine-ligated synthetic porphyrin and heme ligands. Four of 10 designed proteins catalyzed cyclopropanation with an enantiomeric ratio greater than 99:1. A second class of proteins were designed to catalyze a silicon-hydrogen insertion and were optimized by directed evolution in whole cells. The evolved proteins incorporated features unlikely to be generated by computational design alone, including a proline in an α helix. Molecular dynamics simulations showed that as the proteins evolved toward higher activity, their conformational ensembles narrowed to favor more productive conformations. Our work demonstrates that efficient de novo protein catalysts are designable and should be useful for manifold chemical processes.
[前沿资讯 ] 深圳先进院合作构建“微生物特种兵”，可同时降解5种污染物 进入全文
中国科学院深圳先进技术研究院
近年来，合成生物学技术飞速发展为降解菌株的构建提供了可能。科学家们能够通过合成生物技术给微生物设计“智能工具箱”——不仅能给细菌安装多种污染物分解能力，还能让这些功能像“乐高积木”一样精准搭配。基于此，研究团队通过底盘菌株筛选与耐盐机制解析，精准锁定了具有最快繁殖速率、高盐耐受和易基因编辑等特性的理想底盘细胞——耐盐菌株“需钠弧菌（Vmax）”，并基于弧菌类细菌能吸收整合外源DNA的自然转化能力，通过调控基因精准构建可调控的具有高效自然转化能力的菌株VCOD-2。研究人员通过测试发现，这一菌株可高效整合外源DNA片段到细菌基因组，相较于自然界中微生物，转化效率可提升数倍。进一步研究中，研究团队将来自不同物种的降解基因模块进行适配优化，创新开发了迭代自然转化法，利用同源替换策略，将5个功能基因簇迭代整合到细菌基因组中，在单一菌株中构建了覆盖单环到多环化合物的五条人工代谢通路，得到的“微生物特种兵”VCOD-15，可实现五种典型芳香类有机污染物——联苯、苯酚、萘、二苯并呋喃和甲苯的同时降解，涵盖了从单环到多环化合物的广泛底物范围。 研究团队通过实际工业废水样本系统验证了“微生物特种兵”工程菌株VCOD-15从实验室到实际污染场地的全场景降解效果：例如，在污染物降解能力方面，这种“微生物特种兵”展现出多靶点同步处理优势——在48小时内对5种目标污染物的去除率均超60%，其中对联苯实现完全降解（100%），甲苯、二苯并呋喃等复杂污染物降解率近90%，较天然菌株提升2至3倍效能。面对极端工业环境挑战，VCOD-15在盐度高达102.5克每升的氯碱废水中仍保持活性，成功克服了传统菌株“遇盐即失活”的瓶颈；在活性污泥反应器中，12小时内可完全去除高浓度污染物；多平行生物反应器测试显示，48小时内工业废水中污染物残留量均低于检测范围的2%，且菌株在复杂微生物群落中占比稳定（40%以上），体现其强大的环境竞争力。
[学术文献 ] An Artificial Metal-Free Peroxidase Designed Using a Ferritin Cage for Bioinspired Catalysis 进入全文
Angewandte Chemie International Edition
Developing artificial enzymes is challenging because it requires precise design of active sites with well-arranged amino acid residues. Histidine-rich oligopeptides have been recently shown to exhibit peroxidase-mimetic activities, but their catalytic function relies on maintaining unique supramolecular structures. This work demonstrates the design of a specific array of histidine residues on the internal surface of the ferritin cage to function as an active center for catalysis. The crystal structures of the ferritin mutants revealed histidine–histidine interactions, forming well-defined histidine clusters (His-clusters). These mutants exhibit peroxidase-mimetic activities by oxidizing 3,3′,5,5′-tetramethylbenzidine (TMB) in the presence of hydrogen peroxide. Molecular dynamics simulations further highlight the co-localization of TMB and hydrogen peroxide at the histidine-rich clusters, indicating that the confined environment of the ferritin cage enhances their interactions. This study presents a simple yet effective approach to design metal-free artificial enzymes, paving the way for innovations in bioinspired catalysis.
[学术文献 ] Robust enzyme discovery and engineering with deep learning using CataPro 进入全文
Nature Communications
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat/Km). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification.
[学术文献 ] Custom CRISPR—Cas9 PAM variants via scalable engineering and machine learning 进入全文
Nature
Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes1–4 to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing3,5,6. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif7 (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.
[前沿资讯 ] 中科院先进院研究功能基因智能挖掘大模型SYMPLEX推动生物制造与合成生物元件开发 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定量合成生物学全国重点实验室、合成生物学研究所娄春波团队与北京大学定量生物学中心钱珑团队合作在国际学术期刊Science Advances上发表题为"Discovery of Diverse and High-quality mRNA Capping Enzymes through a Language Model-enabled Platform"的研究论文，报道了全球首个面向合成生物学元件挖掘与生物制造应用的大语言模型——"SYMPLEX"，并将SYMPLEX模型应用于mRNA加帽酶基因的挖掘，展示了大语言模型赋能生物制造的巨大潜力。 该模型通过融合领域大语言模型训练、合成生物专家知识对齐和大规模生物信息分析，实现了从海量文献中自动化挖掘功能基因元件，并精准评估其工程化应用潜力。研究团队将SYMPLEX应用于mRNA疫苗生物制造关键酶——加帽酶的挖掘，成功获得多种高性能新型加帽酶。第三方公司实验验证显示，这些酶在催化效率上超越国际头部企业New England Biolabs（NEB）商业化加帽酶2倍以上，显著提升了mRNA疫苗生产率和成本效益。此项成果不仅为合成生物学元件设计提供了AI驱动的新范式，更展现了大语言模型等人工智能技术在生物制造中的广阔应用前景。
