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[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 ] Glyceollin biosynthesis in a plant chassis engineered for isoflavone production 进入全文
Nature Chemical Biology
Glyceollins are structurally complex potent antimicrobial isoflavonoid phytoalexins produced by the crop soybean (Glycine max), yet their biosynthesis remains elusive, making it impossible to carry out synthetic biology-based production and engineering for further development. Here, via assembling synergistic engineering strategies, we successfully rewired the metabolic fluxes in Nicotiana benthamiana leaves for high-yield production of isoflavonoid precursor daidzein (7.04 g kg−1 dry weight (dw)), allowing for efficient screening and identification of six cytochrome P450 monooxygenases, namely glyceollin synthases, that furnish the pyrano/furano E ring and complete the 15-step biosynthetic pathways of diverse glyceollins. We establish that purified glyceollins are important for plant defense as they can effectively suppress the growth of Phytophthora sojae in vitro. Our engineered plant chassis can provide facile access to bioactive isoflavonoids, as manifested by the de novo total biosynthesis of glyceollins (for example, I, II, III and VII at up to 5.9 g kg−1, dw) and medicarpin (0.72 g kg−1, dw) for enhanced pathogen resistance and medicinal value.
[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.
