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Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number ( k cat ), Michaelis constant ( K m ), and catalytic efficiency ( k cat / K m ), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust k cat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications. Prediction of enzyme kinetic parameters is essential for designing and optimising enzymes for various biotechnological and industrial applications. Here, authors presented a prediction framework (UniKP), which improves the accuracy of predictions for three enzyme kinetic parameters.

The advances in determining the total structure of atomically precise metal nanoclusters have prompted extensive exploration into the origins of chirality in nanoscale systems. While chirality is generally transferrable from the surface layer to the metal–ligand interface and kernel, we present here an alternative type of gold nanoclusters (138 gold core atoms with 48 2,4-dimethylbenzenethiolate surface ligands) whose inner structures are not asymmetrically induced by chiral patterns of the outermost aromatic substituents. This phenomenon can be explained by the highly dynamic behaviors of aromatic rings in the thiolates assembled via π − π stacking and C − H···π interactions. In addition to being a thiolate-protected nanocluster with uncoordinated surface gold atoms, the reported Au 138 motif expands the size range of gold nanoclusters having both molecular and metallic properties. Our current work introduces an important class of nanoclusters with intrinsic chirality from surface layers rather than inner structures and will aid in elucidating the transition of gold nanoclusters from their molecular to metallic states. Chiral metal nanoclusters prepared from achiral ligands generally contain chiral kernel structures. Here, the authors report an alternative type of gold nanoclusters whose intrinsic chirality arises solely from the arrangement of the organic components on their surface.

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 ( k c a t ), Michaelis constant ( K m ), and catalytic efficiency ( k c a t / K m ). 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. Enzyme kinetic parameter prediction is a challenge in enzyme discovery and engineering. Here, the authors train a robust deep learning model CataPro to predict enzyme kinetic parameters and validate its practicality through wet-lab experiments.

Synthetic microbial consortia represent a new frontier for synthetic biology given that they can solve more complex problems than monocultures. However, most attempts to co-cultivate these artificial communities fail because of the winner-takes-all in nutrients competition. In soil, multiple species can coexist with a spatial organization. Inspired by nature, here we show that an engineered spatial segregation method can assemble stable consortia with both flexibility and precision. We create microbial swarmbot consortia (MSBC) by encapsulating subpopulations with polymeric microcapsules. The crosslinked structure of microcapsules fences microbes, but allows the transport of small molecules and proteins. MSBC method enables the assembly of various synthetic communities and the precise control over the subpopulations. These capabilities can readily modulate the division of labor and communication. Our work integrates the synthetic biology and material science to offer insights into consortia assembly and serve as foundation to diverse applications from biomanufacturing to engineered photosynthesis. Most attempts to co-cultivate the artificial microbial communities fail mostly due to the mismatched rates of consumption and production of nutrients among subpopulations. Here, the authors develop a microbial swarmbot mediated spatial segregation method to assemble stably coexisting consortia with both flexibility and precision.

Infections caused by antibiotic-resistant bacteria are a rising public health threat and make the identification of new antibiotics a priority. From a cell-based screen for bactericidal compounds against Mycobacterium tuberculosis under nutrient-deprivation conditions we identified auranofin, an orally bioavailable FDA-approved antirheumatic drug, as having potent bactericidal activities against both replicating and nonreplicating M. tuberculosis . We also found that auranofin is active against other Gram-positive bacteria, including Bacillus subtilis and Enterococcus faecalis , and drug-sensitive and drug-resistant strains of Enterococcus faecium and Staphylococcus aureus . Our biochemical studies showed that auranofin inhibits the bacterial thioredoxin reductase, a protein essential in many Gram-positive bacteria for maintaining the thiol-redox balance and protecting against reactive oxidative species. Auranofin decreases the reducing capacity of target bacteria, thereby sensitizing them to oxidative stress. Finally, auranofin was efficacious in a murine model of methicillin-resistant S. aureus infection. These results suggest that the thioredoxin-mediated redox cascade of Gram-positive pathogens is a valid target for the development of antibacterial drugs, and that the existing clinical agent auranofin may be repurposed to aid in the treatment of several important antibiotic-resistant pathogens. Significance The identification of new antibiotics with novel mechanisms of action has become a pressing need considering the growing threat of drug-resistant infections. We have identified auranofin, an FDA-approved drug, as having potent bactericidal activity against Gram-positive pathogenic bacteria. Auranofin inhibits an enzyme, thioredoxin reductase, not targeted by other antibiotics, and thus retains efficacy against many clinically relevant drug-resistant strains, including in a mouse model of infection. Because auranofin is an approved drug, its route to the clinic may be expedited with reduced cost. Our work suggests that auranofin is a candidate for drug repurposing in antibacterial therapy.

Predicting EC numbers for chemical reactions enables efficient enzymatic annotations for computer-aided synthesis planning. However, conventional machine learning approaches encounter challenges due to data scarcity and class imbalance. Here, we introduce CLAIRE ( C ontrastive L earning-based A nnotat I on for R eaction’s E C), a novel framework leveraging contrastive learning, pre-trained language model-based reaction embeddings, and data augmentation to address these limitations. CLAIRE achieved notable performance improvements, demonstrating weighted average F1 scores of 0.861 and 0.911 on the testing set (n = 18,816) and an independent dataset (n = 1040) derived from yeast’s metabolic model, respectively. Remarkably, CLAIRE significantly outperformed the state-of-the-art model by 3.65 folds and 1.18 folds, respectively. Its high accuracy positions CLAIRE as a promising tool for retrosynthesis planning, drug fate prediction, and synthetic biology applications. CLAIRE is freely available on GitHub ( https://github.com/zishuozeng/CLAIRE ). Scientific contribution This work employed contrastive learning for predicting enzymatic reaction’s EC numbers, overcoming the challenges in data scarcity and imbalance. The new model achieves the state-of-the-art performance and may facilitate the computer-aided synthesis planning.

Microbial production of cannabinoids promises to provide a consistent, cheaper, and more sustainable supply of these important therapeutic molecules. However, scaling production to compete with traditional plant-based sources is challenging. Our ability to make strain variants greatly exceeds our capacity to screen and identify high producers, creating a bottleneck in metabolic engineering efforts. Here, we present a yeast-based biosensor for detecting microbially produced Δ 9 -tetrahydrocannabinol (THC) to increase throughput and lower the cost of screening. We port five human cannabinoid G protein-coupled receptors (GPCRs) into yeast, showing the cannabinoid type 2 receptor, CB2R, can couple to the yeast pheromone response pathway and report on the concentration of a variety of cannabinoids over a wide dynamic and operational range. We demonstrate that our cannabinoid biosensor can detect THC from microbial cell culture and use this as a tool for measuring relative production yields from a library of Δ 9 -tetrahydrocannabinol acid synthase (THCAS) mutants. Microbial production of cannabinoids promises a cheaper and more sustainable route to these important therapeutic molecules, but strain improvement and screening is challenging. Here, the authors develop a yeast-based Δ9-tetrahydrocannabinol (THC) biosensor for screening microbial mutant libraries.

Background Saccharomyces cerevisiae is an important synthetic biology chassis for microbial production of valuable molecules. Promoter engineering has been frequently applied to generate more synthetic promoters with a variety of defined characteristics in order to achieve a well-regulated genetic network for high production efficiency. Galactose-inducible (GAL) expression systems, composed of GAL promoters and multiple GAL regulators, have been widely used for protein overexpression and pathway construction in S. cerevisiae . However, the function of each element in synthetic promoters and how they interact with GAL regulators are not well known. Results Here, a library of synthetic GAL promoters demonstrate that upstream activating sequences (UASs) and core promoters have a synergistic relationship that determines the performance of each promoter under different carbon sources. We found that the strengths of synthetic GAL promoters could be fine-tuned by manipulating the sequence, number, and substitution of UASs. Core promoter replacement generated synthetic promoters with a twofold strength improvement compared with the GAL1 promoter under multiple different carbon sources in a strain with GAL1 and GAL80 engineering. These results represent an expansion of the classic GAL expression system with an increased dynamic range and a good tolerance of different carbon sources. Conclusions In this study, the effect of each element on synthetic GAL promoters has been evaluated and a series of well-controlled synthetic promoters are constructed. By studying the interaction of synthetic promoters and GAL regulators, synthetic promoters with an increased dynamic range under different carbon sources are created.

Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.

Glucosinolates (GSLs) and their degradation products in radish confer plant defense, promote human health, and generate pungent flavor. However, the intact GSLs in radish have not been investigated comprehensively yet. Here, an accurate qualitative and quantitative analyses of 15 intact GSLs from radish, including four major GSLs of glucoraphasatin (GRH), glucoerucin (GER), glucoraphenin (GRE), and 4-methoxyglucobrassicin (4MGBS), were conducted using UHPLC-HRMS/MS in combination with UHPLC-QqQ-MS/MS. Simultaneously, three isomers of hexyl GSL, 3-methylpentyl GSL, and 4-methylpentyl GSL were identified in radish. The highest content of GSLs was up to 232.46 μmol/g DW at the 42 DAG stage in the ‘SQY’ taproot, with an approximately 184.49-fold increase compared to the lowest content in another sample. That the GSLs content in the taproots of two radishes fluctuated in a similar pattern throughout the five vegetative growth stages according to the metabolic profiling, whereas the GSLs content in the ‘55’ leaf steadily decreased over the same period. Additionally, the proposed biosynthetic pathways of radish-specific GSLs were elucidated in this study. Our findings will provide an abundance of qualitative and quantitative data on intact GSLs, as well as a method for detecting GSLs, thus providing direction for the scientific progress and practical utilization of GSLs in radish.