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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A new database website for nuclear level densities

We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2021

AI-based language models powering drug discovery and development

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Characterization of two types of polysaccharides from Eremurus hissaricus roots growing in Tajikistan

Eremurus Hissaricus (E.hissaricus) is a native plant of Tajikistan that has been used in traditional medicine and whose raw components have been identified as having potential for the domestic economy. We have found that carbohydrates comprise 80–85% of the root biomass, from which we have characterized two types of polysaccharides: A water-soluble polysaccharide (WSP) and an acid-soluble polysaccharide (ASP), with 23% and 8% yields, respectively. The results from FTIR, HPAEC-PAD, HPSEC, methylation linkage analysis and NMR spectroscopy ( 1 H, 13 C, zTOCSY, HSQC, HSQCTOXY and HMBC) demonstrated that WSP has a backbone of (1 → 4)-linked β-d-mannopyranosyl and β-d-glucopyranosyl residues in a 1.5:1 M ratio, having acetylated groups, with weight-average molecular weights of 495 kDa. Analyses of ASP indicate that it is comprised of d-galactose, d-mannose, and d-glucose in a molar ratio of 1.8: 1.6: 1.0, which is consistent with a branched galactoglucomannan polysaccharide with (1 → 4)-linked β-pyranose configurations, and weight-average molecular weight of 179 kDa. The FTIR absorption bands in the region of 875–890 cm 1 and NMR spectroscopy data confirm the presence of β-pyranose configuration of main polysaccharide backbone in the both ASP and WSP samples. These polysaccharides are an available raw material with the potential for use in a number of industrial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

3D high-fidelity automated neutronics guided optimization of fusion blanket designs

The compact Fusion Pilot Plant (FPP) is defined in the recent National Academies of Sciences, Engineering, and Medicine report as the next step of fusion energy demonstration with a $50$ MWe peak net electricity production, $Q_e$ greater than $1$, and at least $3$ hours of continuous operation. This fusion pilot plant will be a test bed enabling materials, designs, and fuel management assessment, and it will represent an engineering challenge because of its high-fusion power and compact design targets. Previous reactor data is limited to experiments operating in different design space ranges. Therefore, design iterations and assessments should rely on high-fidelity first-principle theoretical and computational models. The high-fidelity integrated modeling of the plasma is a fundamental part of fusion energy research. However, the whole device modeling is often neglected, utilizing low-fidelity, system-level analysis. Recently, the need for high-fidelity multi-physics modeling was recognized, resulting in a selection of integrated tools. Further, autonomous design optimization requires a streamlined framework that perturbs the design point, reruns the analysis, and examines the outputs. However, high-fidelity analysis requires complex geometry specification that is difficult to perturb. This work presents the parametric CAD generation tool TRACER and a new neutronic workflow. TRACER allows the perturbation of the geometry representation, creating geometry files ready for further analysis. The streamlined neutronic workflow allows efficient and accurate calculations. The two new tools coupled together were used to perform a 3D high-fidelity multi-objective, multi-input optimization of an "ARC Class" compact tokamak design. The workflow was driven by an optimization driver for full automation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Rational approach to drug discovery for human schistosomiasis

Human schistosomiasis is a debilitating, life-threatening disease affecting more than 229 million people in as many as 78 countries. There is only one drug of choice effective against all three major species of Schistosoma, praziquantel (PZQ). However, as with many monotherapies, evidence for resistance is emerging in the field and can be selected for in the laboratory. Previously used therapies include oxamniquine (OXA), but shortcomings such as drug resistance and affordability resulted in discontinuation. Employing a genetic, biochemical and molecular approach, a sulfotransferase (SULT-OR) was identified as responsible for OXA drug resistance. By crystallizing SmSULT- OR with OXA, the mode of action of OXA was determined. This information allowed a rational approach to novel drug design. Our team approach with schistosome biologists, medicinal chemists, structural biologists and geneticists has enabled us to develop and test novel drug derivatives of OXA to treat this disease. Using an iterative process for drug development, we have successfully identified derivatives that are effective against all three species of the parasite. One derivative CIDD-0149830 kills 100% of all three human schistosome species within 5 days. The goal is to generate a second therapeutic with a different mode of action that can be used in conjunction with praziquantel to overcome the ever-growing threat of resistance and improve efficacy. The ability and need to design, screen, and develop future, affordable therapeutics to treat human schistosomiasis is critical for successful control program outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Subject-specific multi-scale modeling of the fate of inhaled aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2024

Structural and biochemical basis for regiospecificity of the flavonoid glycosyltransferase UGT95A1

Glycosylation is a predominant strategy plants use to fine-tune the properties of small molecule metabolites to affect their bioactivity, transport, and storage. It is also important in biotechnology and medicine as many glycosides are utilized in human health. Small molecule glycosylation is largely carried out by family 1 glycosyltransferases. Here, we report a structural and biochemical investigation of UGT95A1, a family 1 GT enzyme from Pilosella officinarum that exhibits a strong, unusual regiospecificity for the 3'-O position of flavonoid acceptor substrate luteolin. We obtained an apo crystal structure to help drive the analyses of a series of binding site mutants, revealing that while most residues are tolerant to mutations, key residues M145 and D464 are important for overall glycosylation activity. Interestingly, E347 is crucial for maintaining the strong preference for 3'-O glycosylation, while R462 can be mutated to increase regioselectivity. The structural determinants of regioselectivity were further confirmed in homologous enzymes. Our study also suggests that the enzyme contains large, highly dynamic, disordered regions. We showed that while most disordered regions of the protein have little to no implication in catalysis, the disordered regions conserved among investigated homologs are important to both the overall efficiency and regiospecificity of the enzyme. This report represents a comprehensive in-depth analysis of a family 1 GT enzyme with a unique substrate regiospecificity and may provide a basis for enzyme functional prediction and engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Regulatory helix plays a key role in genetic ON-OFF switching for the 2’-deoxyguanosine sensing mRNA element

Transcriptional riboswitches, noncoding mRNA elements that operate in cis to regulate gene expression, have a promising potential in medicine, synthetic biology and directed evolution. They bind to cellular metabolites or metal ions with high specificity, leading to conformational rearrangements that facilitate the activation or premature termination of transcription for downstream genes. This elegant mechanism for feedback regulation of metabolic pathways has been identified in prokaryotes and a few in eukaryotes. Our chemical probing of the 2’-deoxyguanosine (2’-dG)-sensing riboswitch demonstrates that the overall conformational state of the full-length riboswitch (dGsw-fl) is unresponsive to the 2’-dG. Although binding proceeds as expected, dGsw-fl exclusively populates an OFF state of transcriptional inhibition. We chemically probed the structure of a known dGsw transcriptional intermediate (dGsw-int) to evaluate the possibility of a cotranscriptional regulatory role. Interestingly, apo dGsw-int adopts an alternative conformation in which a stable anti-terminator helix is formed, leading to an ON state where transcription can proceed. In the presence of 2’-dG, this anti-terminator helix is destabilized to produce a conformation reminiscent of the full-length, OFF-state dGsw. Using a fluorescence quenching assay, we demonstrate that binding 2’-dG to early transcriptional intermediates can inhibit the formation of the anti-terminator helix, locking dGsw in an OFF state. These data suggest that metabolite sensing occurs during a brief window of time between the synthesis of two transcriptional intermediates. Our studies indicate that dGsw does not function as a binary ON−OFF switch, but instead fine-tunes the transcription of downstream genes during RNA synthesis using key intermediates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Continuous-time probabilistic models for longitudinal electronic health records

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Best practices in catalysis: A perspective

Catalysis, from its roots in petrochemical refining and conversion, has emerged as a transdisciplinary field that now encompasses synthesis of materials and molecules that enable applications in energy conversion and storage, environmental remediation, medicine, plastics, and fertilizer production, among numerous others. A handful of disciplines can claim relevance and success over such an extended period of time and continue to claim a preeminent role in defining the state-of-the-art in science and technology. Syncretic and rapid advancements in formulation and spectroscopic characterization of materials and molecules useful as catalysts, high-level density functional and molecular orbital theory calculations, and a strong foundation in concepts of physical chemistry, thermodynamics, and chemical kinetics offer new and abundant opportunities at the present day for addressing the grand challenge of controlling chemical transformations using catalysis. Here, we examine what we have learned of concepts that underpin heterogeneous catalysis but more importantly, how we learned to archive our knowledge in context of a set of best practices and standards that have emerged in the course of our learnings—ones we seek to highlight herein. Our perspective emphasizes concepts in synthesis, characterization, kinetics, and theory, because these four elements combined enable description of molecular acts that happen on surfaces and how fast they occur. In revisiting best practices in heterogeneous catalysis in these sub-fields and in authorship and peer review we aspire to augment clarity, reproducibility, and rigor in the science and practice of catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Simplification of the purification of heat stable recombinant low molecular weight proteins and peptides from GST-fusion products

The synthesis and purification of peptides of importance in the fields of research and medicine continue to be a challenging task. Chemical synthesis of oligopeptides, especially those greater than 25 amino acids, is cost prohibitive. On the other hand, several bottlenecks exist in the production of recombinant short peptides in heterologous expression hosts such as Escherichia coli (E. coli). In this paper, a rapid, cost-effective, and reliable method for the production and singlestep-purification of peptides and small proteins was developed. Five peptides and small proteins were overexpressed in E. coli as GST-fusion products in high yields. The recombinant peptides or proteins were successfully purified after enzymatic cleavage with selective heat-induced precipitation of the GST-affinity tag. Qualitative and quantitative analysis using SDS-PAGE and mass spectrometric methods suggest that the recombinant peptides/ proteins were purified to >95% homogeneity. Results of biophysical experiments, including multi-dimensional NMR spectroscopy, show that the purified proteins/ peptides retain their native conformation. Isothermal titration calorimetry studies indicate no significant change in the binding affinity of the heattreated purified product to their interacting partner(s) compared to the recombinant peptides purified by conventional chromatographic procedures without subjecting to heat treatment. In our opinion, the results reported render the purification of recombinant proteins/ peptides of biomedical relevance using our proposed method easy and reliable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Green synthesis and characterization of nontoxic L-methionine capped silver and gold nanoparticles

The simple green method for synthesis of stable L-Methionine (L-Met) capped silver (Ag@LM NPs) and gold (Au@LM NPs) nanoparticles (NPs) without adding any additional reduction agent or stabilizer was developed. Colloidal dispersions were characterized by UV–Vis spectrophotometry. The size and spherical shape of NPs were evaluated by transmission electron microscopy. Their surface covering was confirmed by atomic force microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, and zeta potential measurements. Density functional theory calculations pointed that the preferential adsorption mode of L-Met on both Ag and Au surfaces was a vertical binding geometry via –NH2 group, while horizontal binding mode via [sbnd]S[sbnd] and –NH2 groups is also possible. The genotoxicity (evaluated by the micronucleus assay) of NPs, as well as their effects on some oxidative stress parameters (catalase activity, malondialdehyde level), were assessed in vitro using human peripheral blood cells as a model system. The influence of NPs on the morphology of lymphocyte cells studied using atomic force microscopy revealed that the membrane of cells remained unaffected after the treatment with NPs. When considering the effects of NPs on catalase activity and malondialdehyde level, neither particle type promoted oxidative stress. However, the treatment of lymphocytes with Ag@LM NPs induced a concentration-dependent enhancement of the micronuclei incidence and suppression of the cell proliferation while Au@LM NPs promoted cell proliferation, with no significant effects on micronuclei formation. The Ag@LM NPs were more prone to induce DNA damage than Au@LM NPs, which makes the latter type more suitable for further studies in nano-medicine. © 2019.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Making the most of your electrons: Challenges and opportunities in characterizing hybrid interfaces with STEM

Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated hard-soft interfaces have recently evoked significant interest. Soft matter is typically dominated by structural fluctuations even at room temperature, while hard matter is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices, and metal organic frameworks to name a few, have delivered promising insights into the technologically relevant applications such as catalysis, environmental remediation, optoelectronics, and medicine. The interfacial structure between the hard and soft phases demonstrates features across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing structure–property relationships especially difficult. There are additional considerations at all stages of sample analysis from preparing electron-transparent samples to obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. Here, we discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or molecular-level understanding of these interfaces. These solutions present a host of opportunities for investigating the role interfaces play in this unique class of functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Characterization of the Entry Steps in Diterpenoid Alkaloid Biosynthesis

The diterpenoid alkaloids are a group of specialized metabolites where the terpenoid and alkaloid classes intersect, and which are found primarily within the Aconitum (Wolf’s-Bane) and Delphinium (Larkspur) genera. While there is considerable interest in these compounds for their wide range of bioactivities, their structural complexity poses a significant challenge for chemical synthesis. Biosynthesis offers an appealing alternative for production, however, little progress has been made towards elucidation of their biosynthetic pathways. Here, we employ a comparative transcriptomics approach to identify six enzymatic steps in the biosynthesis of atisinium, conserved across both Delphinium grandiflorum and Aconitum plicatum . Key to this pathway is a reductase which selectively incorporates ethanolamine over ethylamine into the diterpenoid scaffold. While the majority of diterpenoid alkaloids contain an ethylamine moiety, we demonstrate through isotope labeling in Aconitum callus cultures and a computational metabolomics approach that ethanolamine is, unintuitively, the preferred source of nitrogen for these metabolites. Identification of these enzymes and production of a key intermediate in a heterologous host paves the way for biosynthetic production of this group of metabolites with promise for medicinal applications.

biosynthesis↗