The study, published in the journal Advanced Biotechnology, describes a five-enzyme cascade that converts the cheap amino acid L-tyrosine into liquiritigenin through a series of intermediates including p-coumaric acid and isoliquiritigenin. The enzymes themselves come from an eclectic cast of organisms: phenylalanine ammonia-lyase and chalcone isomerase from maize, 4-coumarate-CoA ligase from thale cress, chalcone synthase from soybean, and chalcone reductase from alfalfa. In nature, the pathway is buried inside plant cells, where enzymes cluster into multienzyme complexes amid the crowded cytoplasm. The researchers set out to recreate that spatial intimacy in a test tube, where they could control every variable without worrying about keeping cells alive or diverting resources into biomass.

Cell-free biosynthesis comes in two flavors, and the team exploited both. In cell-free metabolic engineering, or CFME, the pathway enzymes are pre-expressed in bacteria, and crude lysates enriched with the target proteins are mixed directly into the reaction. In cell-free protein synthesis-driven metabolic engineering, or CFPS-ME, the enzymes are manufactured on the spot by a cell-free expression system, adding DNA plasmids encoding each enzyme to a bacterial extract loaded with amino acids, nucleotides, and an energy source. When the group compared the two approaches, CFPS-ME consistently outperformed CFME, producing 4.55 milligrams per liter of liquiritigenin from tyrosine, whereas the lysate-only system lagged behind. The difference likely reflects the higher effective enzyme concentrations achievable when protein synthesis proceeds inside the reaction itself.

The next challenge was choosing the right versions of each enzyme and calibrating their proportions. Because a cascade is only as good as its slowest and least compatible links, the researchers screened homologs for every step, tapping the NCBI database and published catalytic data to shortlist candidates. Chalcone reductase deserved particular scrutiny: it works in concert with chalcone synthase to divert flux toward isoliquiritigenin, and without enough malonyl-CoA, the synthase stalls and the pathway shunts toward naringenin instead of the desired product. The screening revealed an optimal combination of the maize, thale cress, soybean, alfalfa, and maize enzymes, with plasmid concentrations in the cell-free expression system fine-tuned to 35, 15, 20, 20, and 20 nanograms per microliter respectively. Excess chalcone reductase actually proved inhibitory, peaking at 25 milligrams per milliliter of enzyme and declining thereafter, a reminder that more catalyst is not always better chemistry.

With the enzyme roster settled, the team moved to reaction conditions, and here the workflow grew ambitious. They began with one-factor-at-a-time tests, identifying 37 degrees Celsius, pH 8.0, a 36-hour reaction time, and a 75-microliter reaction volume as favorable. A Plackett-Burman design, a statistical screening method that tests each variable at two levels across a small set of experiments, then winnowed thirteen parameters down to the five that mattered most: the concentrations of ZmPAL, GmCHS, and MsCHR, plus pH and reaction volume. Steepest-ascent experiments pushed the yield to 104.42 milligrams per liter. But the interplay among enzymes, cofactors like ATP, CoA, and NADPH, and environmental variables creates a rugged optimization landscape that classical statistical designs traverse only slowly.

That is where machine learning took over. The researchers fed seventy-five data points from their preliminary designs into an iterative loop of data input, model training, Bayesian optimization, and experimental validation. Seven algorithms competed for the job, spanning classics like Random Forest, Support Vector Machine, and Multilayer Perceptron, ensemble methods including XGBoost, LightGBM, and CatBoost, and Gaussian Process Regression, prized for its performance on small datasets. Nested cross-validation, with three-fold tuning inside a nine-fold generalization assessment, guarded against overfitting, and when several models performed comparably the team fused them using Stacking, simple averaging, and non-negative least squares weighting. The Stacking ensemble, with RidgeCV as its meta-learner, ultimately achieved a test R-squared of 0.903, meaning it explained more than ninety percent of the variance in experimental outcomes.

Across three rounds of machine-guided experimentation, the average yield of the top twelve predicted conditions climbed from 106.55 to 125.46 and then 138.11 milligrams per liter, while variability shrank, a signature of genuine convergence rather than lucky guesses. Explainable AI techniques sharpened the picture. SHAP analysis, which decomposes each prediction into the contributions of individual features, identified ZmPAL, reaction volume, ATP, and NADPH as the strongest positive drivers, with ZmPAL alone contributing an estimated 35.8 percent uplift in one prediction round. CoA, tyrosine, and GmCHS showed negative correlations, so the model prescribed less of them. The final recipe, including 4 milligrams per milliliter of ZmPAL, 2.2 millimolar ATP, 2.4 millimolar NADPH, pH 7.3, and 39 degrees Celsius, delivered 155.32 milligrams per liter, a 48.7 percent jump over the statistical-only optimization and more than a doubling of conversion efficiency.

The final act of the study tackled spatial organization, borrowing a trick from biology’s own playbook. The team fused covalent self-assembling peptide tags, including the celebrated SpyTag/SpyCatcher pair along with SnoopTag/SnoopCatcher, CC-Di, and RIAD/RIDD systems, onto the pathway enzymes so that specific pairs could lock together like molecular Velcro. Assembling chalcone synthase and chalcone reductase with these peptide pairs already lifted production in the CFME system, with the best combination reaching 22.69 milligrams per liter. Then came the scaffold. The researchers tested eight candidate scaffold proteins, ranging from bacterial microcompartment shells to viral capsid proteins, and the winner was gamma-prefoldin, a filamentous chaperone from hyperthermophilic archaea, fused to SpyCatcher. Attaching all five enzymes to this scaffold nearly doubled yield relative to unscaffolded reactions, and an N-C-C-N-C binding configuration combined with 5 milligrams per milliliter of scaffold protein pushed the titer to 439.42 milligrams per liter, an overall 2.83-fold enhancement attributable to spatial assembly.

Structural modeling helps explain why the scaffold works so well. Predictions generated with AlphaFold 3 depict the complex as a compact, prism-like architecture in which SpyTag placement exposes every catalytic pocket, verified through active-site analysis with the ProteinsPlus platform. The gamma-prefoldin backbone holds the enzymes close enough that intermediates pass directly from one active site to the next rather than diffusing into the bulk solution, where they would be lost to dilution or side reactions. At 2 millimolar tyrosine, the optimized system converts 85.8 percent of the substrate into product, an efficiency few multi-step biosyntheses achieve in vitro. The results suggest a template for future work: pair automated, model-driven tuning of reaction conditions with rational physical organization of the catalytic machinery, and even pathways that have stalled in living cells can be made to sing in a test tube. For liquiritigenin, and potentially for the wider family of medicinal flavonoids it anchors, the bottleneck may finally be beginning to break.

Subject of Research: Machine learning-guided optimization and scaffold-assisted spatial assembly of a cell-free multi-enzyme system for liquiritigenin biosynthesis

Article Title: Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production

Article References: Liu, F., Zhao, S.-B., Liu, Y.-H., Li, J.-F., Lin, N.-Q., Mutailifu, M., Xu, P., & Liu, J.-Z. (2026). Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production. Advanced Biotechnology, 4(2), Article 12. https://doi.org/10.1007/s44307-026-00103-0

Image Credits: AI Generated

DOI: 10.1007/s44307-026-00103-0

Keywords: liquiritigenin, cell-free biosynthesis, machine learning, Bayesian optimization, enzyme scaffolds, SpyTag/SpyCatcher, gamma-prefoldin, flavonoid biosynthesis, metabolic engineering, CFPS-ME, spatial enzyme assembly, biocatalysis

The research, carried out by an international team working across institutes in Iran and Germany, including the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) in Gatersleben and Shiraz University, took on one of the most persistent challenges in medicinal plant science: connecting visible chemical traits to the underlying genetic code. The team genotyped 175 accessions and populations spanning the genus Glycyrrhiza using genotyping-by-sequencing, a technique that slashes the cost of genome-wide profiling by sequencing only the most informative slices of DNA. From this effort they assembled a catalogue of 38,393 high-quality single-nucleotide polymorphisms, or SNPs—individual letter changes scattered across the genome that serve as signposts for comparative genetics.

The first major finding concerns how licorice species relate to one another. Genetic structure analysis showed that G. glabra, the species that produces the celebrated medicinal root, is mostly distinct from its relatives in the genus. This genetic separation matters for conservationists and breeders alike, because it indicates that G. glabra carries a unique reservoir of variation that cannot simply be recovered from closely related species. Within G. glabra itself, the analysis identified a coherent group of 66 plants drawn from 33 Iranian origins, showing moderate genetic differentiation from other accessions. Iran, with its long tradition of licorice harvesting and its position at the heart of the plant’s natural range, appears to shelter a genetically recognizable and potentially valuable portion of the species’ diversity.

With the population structure established, the researchers turned to the central question: which genomic regions control the accumulation of the plant’s bioactive metabolites? They focused on three medically important traits and deployed genome-wide association mapping, a statistical approach that scans thousands of SNPs for correlations with measured phenotypes across many individuals. The results were striking. Twenty highly significant SNP associations emerged in total. Fifteen of them were linked to glabridin in the cork layer of the root, and three of those were also associated with glabridin in the fleshy texture of the root. Five additional SNPs were associated with liquiritigenin in the cork layer.

Those two compounds deserve attention on their own terms. Glabridin is the signature flavonoid of licorice, prized for anti-inflammatory, skin-brightening, antioxidant, and antimicrobial properties, and it is a staple ingredient in cosmetic and pharmaceutical pipelines worldwide. Liquiritigenin is another flavonoid with documented pharmacological activity and a key position in the flavonoid biosynthetic pathway of the plant. Intriguingly, both molecules are described as players in biotic and abiotic stress responses, meaning the plant likely deploys them as chemical shields against pathogens and harsh environmental conditions. The new genetic associations suggest that the very loci breeders might select for higher medicinal content are also tied to how robustly the plant defends itself—a potentially powerful linkage for developing resilient, potent cultivars simultaneously.

Because association mapping identifies genomic neighborhoods rather than single causal genes, the team followed up by searching for candidate genes near the significant SNPs. The list they assembled reads like a who’s who of plant secondary metabolism regulation. Among them stands MYB6, a transcription factor known to activate flavonoid-biosynthetic genes, making it an obvious lever for engineering or selecting plants that produce more glabridin. Another candidate, an OCTOPUS-like gene, plays a role in the differentiation of primary root protophloem and overall root architecture—a fascinating connection, since the metabolites of interest accumulate in specific root tissues, and the internal anatomy of the root may determine where and how much compound is stored.

The candidate list also includes WRKY transcription factors, a large family of regulators that modulate phenylpropanoid, alkaloid, and terpene pathways. WRKY genes are central switches in plant defense signaling, and their presence near the associated loci reinforces the idea that licorice’s medicinal chemistry is deeply entangled with its stress biology. A fourth candidate, aminodeoxychorismate synthase, adds a further layer of metabolic intrigue, hinting that primary metabolism feeds into the specialized chemistry of the root in ways that association mapping can now expose. Together, these genes sketch a plausible molecular chain running from environmental sensing through transcriptional control to the final accumulation of flavonoids in root tissues.

Methodologically, the study demonstrates how modern genomic tools can transform a traditionally understudied medicinal crop. Genotyping-by-sequencing allowed the team to characterize genetic structure and run association mapping in a genus for which genomic resources have been scarce, without requiring a fully assembled reference genome. The combination of population structure analysis, principal component approaches, and mixed linear models for association testing reflects the current best practice for avoiding false positives caused by population stratification—a notorious pitfall when working with geographically structured plant collections such as this one.

The practical implications reach well beyond the laboratory. Wild and landrace licorice populations face pressure from overharvesting and habitat loss, and cultivated varieties often lag behind wild roots in medicinal compound content. By identifying SNP markers linked to glabridin and liquiritigenin accumulation, the study gives breeders molecular tools to screen seedlings for high-value chemistry long before the roots mature—a process that would otherwise take years of cultivation and costly chemical assays. Marker-assisted selection built on these loci could accelerate the development of improved Glycyrrhiza varieties with enhanced medicinal properties, while the genetic structure data can guide the conservation of the species’ most distinctive populations, including the Iranian group highlighted by the analysis.

For a genus that has served human medicine since antiquity, licorice has been surprisingly slow to enter the genomic era. This study changes that, delivering both a comprehensive picture of genetic diversity across Glycyrrhiza species and a concrete set of molecular handles on the chemistry that makes the root valuable. As demand for plant-derived therapeutics grows and climate change tightens the screws on medicinal crop production, the ability to read—and eventually write—the genetic instructions behind licorice’s chemical defenses may prove to be one of the more consequential developments in medicinal plant genomics of the coming decade.

Subject of Research: Genetic structure and SNP associations with secondary metabolites in Glycyrrhiza species and G. glabra

Article Title: Genetic structure analysis of Glycyrrhiza species and identification of SNP-loci associated with secondary metabolites in G. glabra

Article References: Moghadam, A., Karami, A., Kellert, B., Haghi, R., Esmaeili, H., Himmelbach, A., & Otto, L.-G. (2026). Genetic structure analysis of Glycyrrhiza species and identification of SNP-loci associated with secondary metabolites in G. glabra. BMC Genomics, 27(1), Article 767. https://doi.org/10.1186/s12864-026-13361-y

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13361-y

Keywords: licorice, Glycyrrhiza glabra, SNP, genotyping-by-sequencing, GWAS, glabridin, liquiritigenin, flavonoid biosynthesis, genetic diversity, medicinal plants, MYB6, plant breeding