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Psychrophilic enzymes are primarily produced by microorganisms from extremely low-temperature environments which are known as psychrophiles. Their high efficiency at low temperatures and easy heat inactivation property have attracted extensive attention from various food and industrial bioprocesses. However, the application of these enzymes in molecular biology is still limited. In a previous review, the applications of psychrophilic enzymes in industries such as the detergent additives, the food additives, the bioremediation, and the pharmaceutical medicine, and cosmetics have been discussed. In this review, we discuss the main cold adaptation characteristics of psychrophiles and psychrophilic enzymes, as well as the relevant information on different psychrophilic enzymes in molecular biology. We summarize the mining and screening methods of psychrophilic enzymes. We finally recap the expression of psychrophilic enzymes. We aim to provide a reference process for the exploration and expression of new generation of psychrophilic enzymes.

Establishment of a highly efficient cell factory is imperative for 5-aminolevulinic acid (5-ALA) biomanufacturing.A streamlined workflow is described that enables highly efficient 5-ALA synthase mining.Genome-scale model-guided identification and combination of multiplex targets are reported.An artificial homeostasis was designed for dynamically responding to, and fine-tuning, redox status.Final collaborative optimization resulted in the highest 5-ALA biomanufacturing performance achieved to date. The versatile applications of 5-aminolevulinic acid (5-ALA) across the fields of agriculture, livestock, and medicine necessitate a cost-efficient biomanufacturing process. In this study, we achieved the economic viability of biomanufacturing this compound through a systematic engineering framework. First, we obtained a 5-ALA synthase (ALAS) with superior performance by exploring its natural diversity with divergent evolution. Subsequently, using a genome-scale model, we identified and modified four key targets from distinct pathways in Escherichia coli, resulting in a final enhancement of 5-ALA titers up to 21.82 g/l in a 5-l bioreactor. Furthermore, recognizing that an imbalance of redox equivalents hindered further titer improvement, we developed a dynamic control system that effectively balances redox status and carbon flux. Ultimately, we collaboratively optimized the artificial redox homeostasis system at the transcription level with other cofactors at the feeding level, demonstrating the highest recorded performance to date with a titer of 63.39 g/l for the biomanufacturing of 5-ALA. Graphical abstract [Display omitted] The economic viability of biomanufacturing 5-aminolevulinic acid (5-ALA) was successfully demonstrated in this study, showcasing excellent production performance. The titer reached a record-breaking 63.39 g/l at 44 h, representing the highest reported value to date, with the productivity of 1.44 g/l/h. Although the yield (0.384 mol/mol glucose) was lower than theoretically expected, the significant value of 5-ALA positions our developed cell factory competitively for efficient industrial-scale biomanufacturing. Therefore, no challenges unique to this compound can be identified for full-scale fermentation, particularly considering that our 5-ALA cell factory was derived from a widely utilized Escherichia coli strain. Currently, two primary approaches are used to produce 5-ALA: chemical synthesis and biomanufacturing. Microbial biosynthesis of 5-ALA presents a more facile, environmentally benign, and low-cost alternative. Given that 5-ALA is a non-protein amino acid, we anticipated that the entire biomanufacturing process would be analogous to the biomanufacturing of canonical amino acids. Therefore, although a separation process was not implemented in this study, we believe that our developed 5-ALA cell factory exhibits excellent parameters and represents a highly cost-efficient biomanufacturing process. Within NASA’s Technology Readiness Level (TRL) system, we propose that this 5-ALA cell factory has reached TRL 6, indicating a fully functional prototype suitable for demonstration in real production scenarios. A systematic engineering framework was demonstrated to construct a 5-aminolevulinic acid (5-ALA) cell factory, achieving the economic viability of biomanufacturing this compound with an unprecedented production performance (63.39 g/l). This comprehensive framework encompasses enzyme mining, multitarget engineering, artificial homeostasis design, and collaborative optimization.

Establishing an efficient microbial cell factory is crucial for the biomanufacturing of N-acetylneuraminic acid (NeuAc).Artificial intelligence-aided identification and screening of key enzymes enabled rapid selection of optimal candidates.Integrated pathways enhanced the supply of essential metabolic precursors for NeuAc synthesis.Synergistic use of glucose and glycerol as dual-carbon sources balanced NeuAc production with cellular growth.Collaborative optimization strategies achieved unprecedented NeuAc biomanufacturing performance. The growing demand for N-acetylneuraminic acid (NeuAc) has driven the need for efficient and environmentally sustainable biomanufacturing processes. Microbial fermentation offers a promising route, yet optimizing cell factories with excellent phenotypes remains challenging. Here, we engineered Escherichia coli to enable high-efficiency co-utilization of glucose and glycerol. We refactored two synthetic pathways with the same start and end to enhance N-acetylmannosamine (ManNAc) precursor levels and optimized NeuAc synthase using artificial intelligence (AI) techniques and machine learning (ML) sequence mining. Subsequently, phosphoenolpyruvate (PEP) levels were boosted by capturing carbon flow from competing regeneration pathways, thus balancing the intracellular PEP:ManNAc ratio for improved NeuAc synthesis. Besides glucose, an additional carbon inlet from glycerol was opened, achieving a NeuAc titer of 70.4 g/l in fed-batch fermentation with a productivity of 1.17 g/l/h. This work demonstrates a highly efficient microbial cell factory for the biosynthesis of NeuAc and provides a versatile system engineering strategy applicable to other high-value compounds. [Display omitted] Traditional N-acetylneuraminic acid (NeuAc) biosynthesis via microbial fermentation typically relies on a single carbon source, making it challenging to balance precursor metabolism and avoid carbon flux conflicts that constrain product yields. To overcome these limitations, we applied artificial intelligence to identify key genes and regulatory elements involved in NeuAc synthesis, leading to the discovery of highly efficient N-acetylglucosamine 2-epimerase (AGE) and NeuAc synthase (NeuB). We further engineered Escherichia coli to co-utilize glucose and glycerol by modulating carbon flux through targeted genetic modifications. This dual-carbon source system enabled rewiring of central metabolism, establishing a dynamic balance between growth and precursor supply, and significantly boosting NeuAc production. Based on NASA’s Technology Readiness Level (TRL) framework, this NeuAc-producing strain has achieved TRL 6, representing a functional prototype suitable for pilot-scale implementation. This study leverages artificial intelligence (AI)-driven enzyme mining and metabolic reprogramming for precise precursor control, alongside dual-carbon source synergy, establishing a cost-effective N-acetylneuraminic acid (NeuAc) biomanufacturing platform. This systematic synthetic biotechnology framework not only elevates NeuAc yields, but also offers generalizable solutions to persistent bottlenecks in microbial-based bioproduction.

Deep learning models facilitate the discovery, engineering, and design of novel genome editors.Protein structure prediction and homolog search are effective methods for mining genome editors that have long evaded detection by traditional sequence-based algorithms.AI-driven approaches turn protein engineering from a needle-in-a-haystack challenge into a tractable process, enabling the optimization of properties of genome editors through efficient in silico engineering (i.e., finding the most stabilizing mutations).Computational protein design methods now bypass evolutionary constraints, creating next-generation genome editors with functions unprecedented in nature. Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology. Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology.

Several studies have demonstrated the health-promoting effects of betalains due to their high antioxidant capacity and their positive effect on the dose-dependent inhibition of cancer cells and their proliferation. To date, betalains were restricted to plants of the order Caryophyllales and some species of fungi, but the present study reveals the first betalain-producing bacterium, as well as the first steps in the formation of pigments. This finding demonstrates that betalain biosynthesis can be expanded to prokaryotes. The biosynthesis of antioxidant pigments, namely, betalains, was believed to be restricted to Caryophyllales plants. This paper changes this paradigm, and enzyme mining from bacterial hosts promoted the discovery of bacterial cultures producing betalains. The spectrum of possible sources of betalain pigments in nature is broadened by our description of the first betalain-forming bacterium, Gluconacetobacter diazotrophicus . The enzyme-specific step is the extradiol cleavage of the precursor amino acid l -dihydroxyphenylalanine ( l -DOPA) to form the structural unit betalamic acid. Molecular and functional work conducted led to the characterization of a novel dioxygenase, a polypeptide of 17.8 kDa with a K m of 1.36 mM, with higher activity and affinity than those of its plant counterparts. Its superior activity allowed the first experimental characterization of the early steps in the biosynthesis of betalains by fully characterizing the presence and time evolution of 2,3- and 4,5-seco-DOPA intermediates. Furthermore, spontaneous chemical reactions are characterized and incorporated into a comprehensive enzymatic-chemical mechanism that yields the final pigments. IMPORTANCE Several studies have demonstrated the health-promoting effects of betalains due to their high antioxidant capacity and their positive effect on the dose-dependent inhibition of cancer cells and their proliferation. To date, betalains were restricted to plants of the order Caryophyllales and some species of fungi, but the present study reveals the first betalain-producing bacterium, as well as the first steps in the formation of pigments. This finding demonstrates that betalain biosynthesis can be expanded to prokaryotes.

Background Computational mining of useful enzymes and biosynthesis pathways is a powerful strategy for metabolic engineering. Through systematic exploration of all conceivable combinations of enzyme reactions, including both known compounds and those inferred from the chemical structures of established reactions, we can uncover previously undiscovered enzymatic processes. The application of the novel alternative pathways enables us to improve microbial bioproduction by bypassing or reinforcing metabolic bottlenecks. Benzylisoquinoline alkaloids (BIAs) are a diverse group of plant-derived compounds with important pharmaceutical properties. BIA biosynthesis has developed into a prime example of metabolic engineering and microbial bioproduction. The early bottleneck of BIA production in Escherichia coli consists of 3,4-dihydroxyphenylacetaldehyde (DHPAA) production and conversion to tetrahydropapaveroline (THP). Previous studies have selected monoamine oxidase (MAO) and DHPAA synthase (DHPAAS) to produce DHPAA from dopamine and oxygen; however, both of these enzymes produce toxic hydrogen peroxide as a byproduct. Results In the current study, in silico pathway design is applied to relieve the bottleneck of DHPAA production in the synthetic BIA pathway. Specifically, the cytochrome P450 enzyme, tyrosine N -monooxygenase (CYP79), is identified to bypass the established MAO- and DHPAAS-mediated pathways in an alternative arylacetaldoxime route to DHPAA with a peroxide-independent mechanism. The application of this pathway is proposed to result in less formation of toxic byproducts, leading to improved production of reticuline (up to 60 mg/L at the flask scale) when compared with that from the conventional MAO pathway. Conclusions This study showed improved reticuline production using the bypass pathway predicted by the M-path computational platform. Reticuline production in E. coli exceeded that of the conventional MAO-mediated pathway. The study provides a clear example of the integration of pathway mining and enzyme design in creating artificial metabolic pathways and suggests further potential applications of this strategy in metabolic engineering.

Protists represent a vast yet underexplored reservoir of enzymatic diversity across the eukaryotic tree of life. In this study, we established axenic strains of diverse protists from four major eukaryotic supergroups using single-cell isolation and generated de novo transcriptomes for each strain, as reference genomes or transcriptomes are not available for these strains. As a test case for industrial enzyme discovery, we targeted nine enzyme classes used in pulp processing and evaluated whether protist-derived sequences occupy underrepresented sequence space relative to major public databases. Functional annotation combined with Sequence Similarity Network analysis revealed multiple clusters composed exclusively of protist-origin sequences, indicating candidate enzymes with high sequence-level novelty. These results suggest that protists may provide a practical resource for expanding the repertoire of industrially relevant enzymes and prioritizing targets for further characterization. However, additional in silico analyses and experimental validation will be required to determine whether these sequence-divergent candidates exhibit properties that meet industrial requirements.

Background Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature–based enzyme functional prediction tool to assign Enzyme Commission (EC) digits. Results DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes. Conclusions Our results offer preliminarily confirmation of the existence of the hypothesized huge number of “hidden enzymes” in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results highlight the necessity of using more advanced computational tools than BLAST in protein database annotations to extract additional biologically relevant functional information from the available biological sequences.

The rapidly expanding body of biomedical literature encompasses a wealth of information concerning the pharmacological effects, mechanisms of action, adverse reactions, and repurposing potential of small-molecule therapeutics. Nevertheless, the systematic extraction and integration of this knowledge continue to pose substantial challenges. In this study, we propose an integrated text-mining framework for the automated extraction and structured representation of information on the biological activities of low-molecular-weight compounds, exemplified by angiotensin-converting enzyme (ACE) inhibitors as a representative pharmacological class. A corpus comprising over 20,000 PubMed titles and abstracts reporting in vitro, in vivo, and clinical investigations of ACE inhibitors was assembled. Chemical compounds, proteins/genes, and diseases were recognized using a previously developed named entity recognition model based on conditional random fields. Entity-level associations were extracted at the sentence level through a rule-based approach employing manually curated pattern phrases, followed by normalization via automated queries to PubChem, UniProt, and the Human Disease Ontology. The proposed methodology facilitated the extraction of approximately 22,000 unique and normalized associations encompassing drug-target, drug-disease, and drug-drug relationships. In addition to confirming well-established therapeutic effects and clinically recognized drug combinations, the analysis identified underexplored pharmacological activities of ACE inhibitors, including antineoplastic, antifibrotic, and neuropsychiatric properties, along with mechanistic associations involving matrix metalloproteinases and neurotrophic signaling pathways. Collectively, these findings underscore the potential of automated literature mining to advance systematic knowledge integration and data-driven hypothesis generation in the contexts of drug repurposing and safety evaluation.

Bioactive natural products have evolved to inhibit specific cellular targets and have served as lead molecules for health and agricultural applications for the past century 1 – 3 . The post-genomics era has brought a renaissance in the discovery of natural products using synthetic-biology tools 4 – 6 . However, compared to traditional bioactivity-guided approaches, genome mining of natural products with specific and potent biological activities remains challenging 4 . Here we present the discovery and validation of a potent herbicide that targets a critical metabolic enzyme that is required for plant survival. Our approach is based on the co-clustering of a self-resistance gene in the natural-product biosynthesis gene cluster 7 – 9 , which provides insight into the potential biological activity of the encoded compound. We targeted dihydroxy-acid dehydratase in the branched-chain amino acid biosynthetic pathway in plants; the last step in this pathway is often targeted for herbicide development 10 . We show that the fungal sesquiterpenoid aspterric acid, which was discovered using the method described above, is a sub-micromolar inhibitor of dihydroxy-acid dehydratase that is effective as a herbicide in spray applications. The self-resistance gene astD was validated to be insensitive to aspterric acid and was deployed as a transgene in the establishment of plants that are resistant to aspterric acid. This herbicide-resistance gene combination complements the urgent ongoing efforts to overcome weed resistance 11 . Our discovery demonstrates the potential of using a resistance-gene-directed approach in the discovery of bioactive natural products. Fungal genome mining targeted to self-resistance genes close to biosynthetic gene clusters identifies a pathway that produces aspterric acid, which proves to be a potent inhibitor of plant growth.