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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A systematic multicriteria-based approach to support product portfolio selection in microalgae biorefineries

Here this work proposes and applies a sequential approach of objective methods to aid the decision-making process for the deployment of microalgae biorefineries. The strategy combines Multicriteria Decision Analysis (MCDA) and weight assignment methods to simultaneously consider technical, economic, and environmental criteria to (1) outrank the best bioproduct options from different biomass fractions present in microalgae biomass at different ratios (namely carbohydrates, lipids, and protein) and (2) define the most suitable biorefining pathways associated with specific pairings of microalgae strains and cultivation conditions. The first part of the assessment identified succinic acid, acrylic acid, and citric acid as the top-ranked bioproducts from carbohydrates, polyurethane from lipids, and thermoplastic extrusion co-feed from protein. The second step of the analysis determined that, when production of a hydrocarbon fuel is desired, the compositional profile of a strain is paramount in defining the biorefining setup that should be pursued. In summary, microalgae lipids should be sent to the production of hydrocarbon fuels if the ratio between neutral lipids and fermentable carbohydrates is higher than roughly 1, with carbohydrates and protein being converted to the higher-value products noted above. Finally, this result was corroborated through process simulations, which indicated superior economic and environmental metrics when strains are paired with suitable conversion pathways identified through MCDA based on their compositional profiles. The outcomes of this work provide clear, objective, guidelines for establishing the best biorefining approach for a large suite of biochemical compositions as a screening method prior to employing detailed process simulations alongside rigorous techno-economic and life-cycle assessments.

09 BIOMASS FUELS↗
DOE OSTI2020

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Tensor network representation of non-abelian gauge theory coupled to reduced staggered fermions

We show how to construct a tensor network representation of the path integral for reduced staggered fermions coupled to a non-abelian gauge field in two dimensions. The resulting formulation is both memory and computation efficient because reduced staggered fermions can be represented in terms of a minimal number of tensor indices while the gauge sector can be approximated using Gaussian quadrature with a truncation. Numerical results obtained using the Grassmann TRG algorithm are shown for the case of SU(2) lattice gauge theory and compared to Monte Carlo results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Structure and dynamics of SARS-CoV-2 proofreading exoribonuclease ExoN

High-fidelity replication of the large RNA genome of coronaviruses (CoVs) is mediated by a 3'-to-5' exoribonuclease (ExoN) in nonstructural protein 14 (nsp14), which excises nucleotides including antiviral drugs misincorporated by the low-fidelity viral RNA-dependent RNA polymerase (RdRp) and has also been implicated in viral RNA recombination and resistance to innate immunity. Here, we determined a 1.6-Å resolution crystal structure of severe acute respiratory syndrome CoV 2 (SARS-CoV-2) ExoN in complex with its essential cofactor, nsp10. The structure shows a highly basic and concave surface flanking the active site, comprising several Lys residues of nsp14 and the N-terminal amino group of nsp10. Modeling suggests that this basic patch binds to the template strand of double-stranded RNA substrates to position the 3' end of the nascent strand in the ExoN active site, which is corroborated by mutational and computational analyses. We also show that the ExoN activity can rescue a stalled RNA primer poisoned with sofosbuvir and allow RdRp to continue its extension in the presence of the chain-terminating drug, biochemically recapitulating proofreading in SARS-CoV-2 replication. Molecular dynamics simulations further show remarkable flexibility of multidomain nsp14 and suggest that nsp10 stabilizes ExoN for substrate RNA binding to support its exonuclease activity. Our high-resolution structure of the SARS-CoV-2 ExoN–nsp10 complex serves as a platform for future development of anticoronaviral drugs or strategies to attenuate the viral virulence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy

The spectral invariants theory ( p -theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the p-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. Further, the coefficient of determination (R²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3% for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4% for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3% to 42.6% for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.

3D heterogeneous structure↗
DOE OSTI2025

RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves

Radiative transfer models (RTMs) describe how light is absorbed, scattered, and transmitted within leaves by simulating mechanistic light propagation processes. The PROSPECT model is based on measurable parameters (the leaf biochemical content) and a non-measurable parameter (the leaf anatomical structure represented by the leaf structure parameter (N)). The effect of N on the optical properties of leaves has been investigated through a number of local and global sensitivity analyses. Other studies have directly evaluated the effect of the leaf anatomical structure on spectral reflectance, particularly in the near infrared region. However, the relationship between N and the anatomical structure is unclear. Here, in this study, we leveraged eLeaf, a ray tracing-based 3D rice leaf simulator, to establish relationships between leaf anatomical features and spectral properties, enabling us to replace N in the PROSPECT-4 model with measurable leaf anatomical parameters and develop the RSPECT model. The leaf thickness at minor vein, leaf thickness at bulliform cells, mesophyll thickness at minor vein, and distance between two minor veins could be used to predict N effectively. The RSPECT model achieved spectral simulation accuracy comparable to PROSPECT-4 and was more suitable for parameter inversion of the physical and chemical properties of rice leaves, with relative root mean square errors of 7.4% for chlorophyll content, 5.6% for equivalent water thickness, and 7.5% for dry matter content. In conclusion, RSPECT improves radiative transfer modeling by integrating measurable anatomical features and provides a framework for extending this approach to other vegetation types.

hyperspectral↗
DOE OSTI2022

An automated workflow that generates atom mappings for large‐scale metabolic models and its application to Arabidopsis thaliana

SUMMARY Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large‐scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large‐scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana . We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)‐containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N‐MFA studies of A. thaliana . The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph‐theoretic structural analyses with large‐scale metabolic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Revealing a Hidden Intermediate of Rotatory Catalysis with X-ray Crystallography and Molecular Simulations

The mechanism of rotatory catalysis in ATPhydrolyzing molecular motors remains an unresolved puzzle in biological energy transfer. Notwithstanding the wealth of available biochemical and structural information inferred from years of experiments, knowledge on how the coupling between the chemical and mechanical steps within motors enforces directional rotatory movements remains fragmentary. Even more contentious is to pinpoint the rate-limiting step of a multistep rotation process. Here, using vacuolar or V 1 -type hexameric ATPase as an exemplary rotational motor, we present a model of the complete 4-step conformational cycle involved in rotatory catalysis. First, using Xray crystallography, a new intermediate or “dwell” is identified, which enables the release of an inorganic phosphate (or P i ) after ATP hydrolysis. Using molecular dynamics simulations, this new dwell is placed in a sequence with three other crystal structures to derive a putative cyclic rotation path. Free-energy simulations are employed to estimate the rate of the hexameric protein transformations and delineate allosteric effects that allow new reactant ATP entry only after hydrolysis product exit. An analysis of transfer entropy brings to light how the side-chain-level interactions transcend into larger-scale reorganizations, highlighting the role of the ubiquitous arginine-finger residues in coupling chemical and mechanical information. An inspection of all known rates encompassing the 4-step rotation mechanism implicates the overcoming of the ADP interactions with V 1 -ATPase to be the rate-limiting step of motor action.

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

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗
DOE OSTI2023

Multidisciplinary studies with mutated HIV-1 capsid proteins reveal structural mechanisms of lattice stabilization

Abstract HIV-1 capsid (CA) stability is important for viral replication. E45A and P38A mutations enhance and reduce core stability, thus impairing infectivity. Second-site mutations R132T and T216I rescue infectivity. Capsid lattice stability was studied by solving seven crystal structures (in native background), including P38A, P38A/T216I, E45A, E45A/R132T CA, using molecular dynamics simulations of lattices, cryo-electron microscopy of assemblies, time-resolved imaging of uncoating, biophysical and biochemical characterization of assembly and stability. We report pronounced and subtle, short- and long-range rearrangements: (1) A38 destabilized hexamers by loosening interactions between flanking CA protomers in P38A but not P38A/T216I structures. (2) Two E45A structures showed unexpected stabilizing CA NTD -CA NTD inter-hexamer interactions, variable R18-ring pore sizes, and flipped N-terminal β-hairpin. (3) Altered conformations of E45A a α9-helices compared to WT, E45A/R132T, WT PF74 , WT Nup153 , and WT CPSF6 decreased PF74, CPSF6, and Nup153 binding, and was reversed in E45A/R132T. (4) An environmentally sensitive electrostatic repulsion between E45 and D51 affected lattice stability, flexibility, ion and water permeabilities, electrostatics, and recognition of host factors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon-climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool-based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool-based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. In this study, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase

Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Spatial modeling algorithms for reactions and transport in biological cells

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unraveling metabolism underpinning biomass composition shift in Scenedesmus obliquus under simulated outdoor conditions using 13 C-fluxomics

To render the resulting biomass more attractive and amenable for utilization as the basis for low-carbon intensity bioproducts, single-celled algae need to be biochemically and metabolically poised to assimilate and store the delivered carbon in the fastest and most efficient manner. Accelerating biochemical carbon storage, as primarily carbohydrates or lipids, is critical to achieve the high carbon capture potential that is assigned to algae. To guide strain optimization and engineering for maximizing carbon capture and storage, it is essential to elucidate the link between carbon metabolism and biomass composition. Most published metabolomics work in algae remains largely restricted to ideal and simplified environmental conditions in model organisms, thereby limiting their translation to outdoor implementation. In this work, we utilize 13 C isotopic labeling to characterize distinct intracellular metabolic fluxes before, during, and after nitrogen depletion-induced compositional shifts in Scenedesmus obliquus UTEX 393. The results indicate that a transition to carbohydrates is characterized by diverting flux to starch instead of replenishing the Calvin cycle for CO 2 fixation whereas the subsequent transition to lipids is fueled by NADPH produced by upregulating the phosphoenolpyruvate carboxylase (PEPC)–malic enzyme (ME) cycle flux. Our work highlights bottlenecks to carbohydrate- and lipid-rich biomass and can guide implementable strategies to control the fate of fixed carbon in S. obliquus.

09 BIOMASS FUELS↗
DOE OSTI2022

Enzymatic Nitrogen Insertion into Unactivated C–H Bonds

Selective functionalization of aliphatic C–H bonds, ubiquitous in molecular structures, could allow ready access to diverse chemical products. While enzymatic oxygenation of C–H bonds is well established, the analogous enzymatic nitrogen functionalization is still unknown; nature is reliant on pre-oxidized compounds for nitrogen incorporation. Likewise, synthetic methods for selective nitrogen derivatization of unbiased C–H bonds remain elusive. In this work, new-to-nature heme-containing nitrene transferases were used as starting points for the directed evolution of enzymes to selectively aminate and amidate unactivated C(sp3)–H sites. The desymmetrization of methyl- and ethylcyclohexane with divergent site selectivity is offered as demonstration. The evolved enzymes in these lineages are highly promiscuous and show activity towards a wide array of substrates, providing a foundation for further evolution of nitrene transferase function. Computational studies and kinetic isotope effects (KIEs) are consistent with a stepwise radical pathway involving an irreversible, enantiodetermining hydrogen atom transfer (HAT), followed by a lower-barrier diastereoselectivity determining radical rebound step. In-enzyme molecular dynamics (MD) simulations reveal a predominantly hydrophobic pocket with favorable dispersion interactions with the substrate. Here, by offering a direct path from saturated precursors, these enzymes present a new biochemical logic for accessing nitrogen-containing compounds.

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

Visualizing group II intron dynamics between the first and second steps of splicing

Group II introns are ubiquitous self-splicing ribozymes and retrotransposable elements evolutionarily and chemically related to the eukaryotic spliceosome, with potential applications as gene-editing tools. Recent biochemical and structural data have captured the intron in multiple conformations at different stages of catalysis. Here, we employ enzymatic assays, X-ray crystallography, and molecular simulations to resolve the spatiotemporal location and function of conformational changes occurring between the first and the second step of splicing. We show that the first residue of the highly-conserved catalytic triad is protonated upon 5’-splice-site scission, promoting a reversible structural rearrangement of the active site (toggling). Protonation and active site dynamics induced by the first step of splicing facilitate the progression to the second step. Our insights into the mechanism of group II intron splicing parallels functional data on the spliceosome, thus reinforcing the notion that these evolutionarily-related molecular machines share the same enzymatic strategy.

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

TEA Modeling to Quantify Economic Implications for Biorefinery Processing of Isolated Anatomical Fractions of Corn Stover

The Feedstock-Conversion Interface Consortium (FCIC; https://www.energy.gov/sites/prod/files/2020/01/f70/beto-fcic-overview-web.pdf), a collaboration of nine national laboratory partners, seeks to understand impacts of feedstock attributes on biorefinery performance. It is hypothesized that different individual anatomical fractions of corn stover vary in composition and recalcitrance, such that processing each fraction on its own through dedicated campaigns may enable better biorefinery economics overall relative to processing the whole stover material. This presentation focuses on techno-economic analysis (TEA) modeling to quantify the yield and cost ramifications for processing isolated anatomical fractions of corn stover through a low-temperature conversion biorefinery, reflecting a biochemical processing pathway consisting of biomass deconstruction through pretreatment and enzymatic hydrolysis, sugar fermentation and upgrading to hydrocarbon fuels, and lignin upgrading to value-added coproducts. Commercial-scale process simulation and economic evaluation leveraged experimental and analytical data from FCIC researchers for conversion of whole corn stover plus three individual anatomical fractions (cobs, husks, and stalks) across key steps of the conversion process. Our assessment found encouraging potential for biorefinery economic gains that may be achieved through this approach. TEA results indicated fuel yields varying from 29-44 gallons gasoline equivalent (GGE)/dry ton for the individual anatomical fractions compared to whole stover at 34 GGE/ton, equating to minimum fuel selling prices (MFSPs) between $6.37-$10.18/GGE for the fractions versus $8.76/GGE for whole stover (when lignin is burned), or $9.15-$15.19/GGE for the fractions versus $13.11/GGE for whole stover (when lignin is upgraded to coproducts, based on current experimental performance levels). Cobs and husks demonstrated the ability to achieve the highest fuel yields and lowest MFSPs, outperforming whole stover, while stalks led to the opposite result, as a composite reflection of compositional differences and process convertibility. Notably, even when taking the weighted average of the results reflecting each anatomical fraction weighted by its corresponding makeup of corn stover, this feasibility TEA screening supports feedstock cost allowances on the order of roughly $22-$29/ton as may reflect accommodating additional biomass fractionation equipment during feedstock pre-processing upstream of the conversion biorefinery gate to separate corn stover into such constituent fractions. Or viewed differently, the weighted average MFSP for the fractions was found to be $0.31-$0.32/GGE lower than the MFSP for the whole stover basis across either lignin scenario, when maintaining a fixed biomass feedstock cost. These findings highlight favorable implications for biorefinery economics as may be achieved by moving to a staged campaign approach for processing different corn stover fractions sequentially. Further opportunities exist for future work to fill in data gaps for remaining anatomical constituents (e.g. leaves) that were not included in the initial experimental studies, though are expected to maintain similar trends.

biochemical processing pathway↗
DOE OSTI2024

Neutrons in Structural Biology: Challenges and Opportunities (Workshop Report)

Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.

59 BASIC BIOLOGICAL SCIENCES↗