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[学术文献 ] Directed evolution of aminoacyl-tRNA synthetases through in vivo hypermutation 进入全文
Nature Communications
Genetic code expansion (GCE) is a critical approach to the site-specific incorporation of non-canonical amino acids (ncAAs) into proteins. Central to GCE is the development of orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs wherein engineered aaRSs recognize chosen ncAAs and charge them onto tRNAs that decode blank codons (e.g., the amber stop codon). However, evolving new aaRS/tRNA pairs traditionally relies on a labor-intensive process that often yields aaRSs with suboptimal ncAA incorporation efficiencies. Here, we present an OrthoRep-mediated strategy for aaRS evolution, which we demonstrate in 8 independent aaRS evolution campaigns, yielding multiple aaRSs that incorporate an overall range of 13 ncAAs tested. Some evolved systems enable ncAA-dependent translation at single amber codons with similar efficiency as natural translation at sense codons. Additionally, we discover an aaRS that regulated its own expression to enhance ncAA dependency. These findings demonstrate the potential of OrthoRep-driven aaRS evolution platforms to advance the field of GCE.
[学术文献 ] Mechanistic rules for de novo design of enzymes 进入全文
Chem Catalysis
While the last two decades have witnessed the development of a number of different strategies to build synthetic nanomotors that deliver mechanical work, making systems that possess engineered catalytic functionality has not so far been demonstrated either theoretically or experimentally in the context of (wet) molecular nanotechnology. We describe a fundamentally new paradigm in the bottom-up design of systems that give direction to chemistry, which will enable future technologies to control how catalytic activity is organized. Our work is inspired by the key observation that the non-equilibrium dynamics of an enzyme during catalysis simultaneously involve energy transduction and conformational changes, i.e., displacements. This suggests that mechanical considerations should play a key role in the stochastic dynamics of an enzyme, and consequently, in its optimal design with the aim of achieving the desired catalytic cycle. Our proposed dynamical paradigm, built on appropriate implementation of the relevant physical constraints on the minimal reaction coordinates, allows us to identify the following three golden rules for the optimal function of a fueled enzyme driven by mechanochemical coupling: (1) the enzyme and the molecule should be attached at the smaller end of each (i.e., friction matching); (2) the conformational change of the enzyme must be comparable to or larger than the conformational change required of the molecule; and (3) the conformational change of the enzyme must be fast enough so that the molecule actually stretches, rather than just following the enzyme without stretching. The mechanistic rules can provide useful input to the complementary perspectives of de novo enzyme design based on machine learning, as they can be used for training the algorithm, as well as fine-tuning the force fields and phenomenological parameters in all-atom simulations.
[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。
[学术文献 ] RiboJ-assisted non-repeated sgRNA arrays for enhanced CRISPR multiplex genome engineering in Escherichia coli 进入全文
Chemical Engineering Journal
CRISPR-based systems have revolutionized genome editing by enabling precise and efficient genetic modifications. However, achieving multiplex genome editing remains challenging due to limitations in encoding, transcribing, and processing multiple single-guide RNAs (sgRNAs) in repetitive DNA arrays. In this study, we present the RiboJ-Aided Multiplexed Base Editing (RAMBE) system and its advanced iteration, the Non-Repetitive RAMBE (NR-RAMBE) system, designed for efficient and scalable multiplex genome engineering in Escherichia coli. The RAMBE system leverages RiboJ insulators to autonomously process sgRNA arrays, enhancing sgRNA maturation and enabling simultaneous multi-gene editing. We demonstrate editing of up to six endogenous genes in E. coli Nissle 1917 (EcN) in a single step, achieving high target-specific efficiencies of up to 100%, depending on the target and context. This multiplex editing enabled robust butyrate production and improved acetate utilization in engineered EcN strains. Building on this, the NR-RAMBE system incorporates diverse ribozymes and engineered non-repetitive sgRNA handles to minimize sequence repetition. This design reduced synthesis complexity and enabled simultaneous editing of six genomic loci with efficiencies comparable to those of the RAMBE system. The NR-RAMBE system broadens the scope of CRISPR multiplexing by allowing precise and scalable genome editing without labor-intensive sgRNA array assembly, paving the way for diverse large-scale genomic applications.
