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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 91 records · Page 5

Precision Labeling of Native Antibodies with Lock Coupling

The formation of stable protein complexes enables much of biotechnology, but even high-affinity complexes can dissociate, limiting potential applications in biomaterials, bioimaging, nanomedicine, and other protein-based technologies. Here, in this study, we describe lock coupling, a simple and selective one-step reaction between interfacial lysine and glutamate or aspartate side chains to form stable isopeptide bonds and be used for the precise labeling of native antibodies. We identify conditions in which short-lived activated esters formed by the aqueous carbodiimide EDC promote isopeptide bond formation specifically at preassociated amine-acid pairs. Indiscriminate cross-linking is minimized by formation of protein complexes before addition of catalyst, use of acidic pH to suppress exposed Lys reactivity, and limiting the aqueous stability of activated esters. For native antibody (Ab) labeling, we show that the small IgG-binding protein GB1 can be covalently attached to the Ab Fc domain and that introduction of Cys into GB1 loops allows for facile conjugation of fluorophores, micelles, or inorganic nanocrystals for imaging in live cells and animals. By varying Cys substituents and protein stoichiometry, a defined number of probes can be uniformly attached without the need for extensive purification. In live-cell confocal microscopy, labeled GB1 serves as a stable replacement for secondary Abs, enabling simple multicolor immunostaining and imaging. Lock coupling requires just a single reagent in aqueous buffer and leverages both the innate ability of proteins to form high-affinity complexes and the widespread presence of Lys-Glu/Asp pairs at their interfaces, with the potential for precision synthesis of protein-based probes for imaging, biomaterials, biophysics, and medicine.

antibody↗

Trends and Drivers of Terrestrial Sources and Sinks of Carbon Dioxide: An Overview of the TRENDY Project

The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land-atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project, quantifies land biophysical exchange processes and biogeochemistry cycles in support of the annual Global Carbon Budget assessments and the REgional Carbon Cycle Assessment and Processes, phase 2 project. DGVMs use a common protocol and set of driving data sets. A set of factorial simulations allows attribution of spatio-temporal changes in land surface processes to three primary global change drivers: changes in atmospheric CO 2 , climate change and variability, and Land Use and Land Cover Changes (LULCC). Here, we describe the TRENDY project, benchmark DGVM performance using remote-sensing and other observational data, and present results for the contemporary period. Simulation results show a large global carbon sink in natural vegetation over 2012–2021, attributed to the CO 2 fertilization effect (3.8 ± 0.8 PgC/yr) and climate (–0.58 ± 0.54 PgC/yr). Forests and semi-arid ecosystems contribute approximately equally to the mean and trend in the natural land sink, and semi-arid ecosystems continue to dominate interannual variability. The natural sink is offset by net emissions from LULCC (–1.6 ± 0.5 PgC/yr), with a net land sink of 1.7 ± 0.6 PgC/yr. Despite the largest gross fluxes being in the tropics, the largest net land-atmosphere exchange is simulated in the extratropical regions.

58 GEOSCIENCES↗

Identification and characterization of small molecule inhibitors of the LINE-1 retrotransposon endonuclease

The long interspersed nuclear element-1 (LINE-1 or L1) retrotransposon is the only active autonomously replicating retrotransposon in the human genome. L1 harms the cell by inserting new copies, generating DNA damage, and triggering inflammation. Therefore, L1 inhibition could be used to treat many diseases associated with these processes. Previous research has focused on inhibition of the L1 reverse transcriptase due to the prevalence of well-characterized inhibitors of related viral enzymes. Here we present the L1 endonuclease as another target for reducing L1 activity. We characterize structurally diverse small molecule endonuclease inhibitors using computational, biochemical, and biophysical methods. We also show that these inhibitors reduce L1 retrotransposition, L1-induced DNA damage, and inflammation reinforced by L1 in senescent cells. These inhibitors could be used for further pharmacological development and as tools to better understand the life cycle of this element and its impact on disease processes.

60 APPLIED LIFE SCIENCES↗

Structure of an RNA G-quadruplex from the West Nile virus genome

Potential G-quadruplex sites have been identified in the genomes of DNA and RNA viruses and proposed as regulatory elements. The genus Orthoflavivirus contains arthropod-transmitted, positive-sense, single-stranded RNA viruses that cause significant human disease globally. Computational studies have identified multiple potential G-quadruplex sites that are conserved across members of this genus. Subsequent biophysical studies established that some G-quadruplexes predicted in Zika and tickborne encephalitis virus genomes can form and known quadruplex binders reduced viral yields from cells infected with these viruses. The susceptibility of RNA to degradation and the variability of loop regions have made structure determination challenging. Despite these difficulties, we report a high-resolution structure of the NS5-B quadruplex from the West Nile virus genome. Analysis reveals two stacked tetrads that are further stabilized by a stacked triad and transient noncanonical base pairing. This structure expands the landscape of solved RNA quadruplex structures and demonstrates the diversity and complexity of biological quadruplexes. We anticipate that the availability of this structure will assist in solving further viral RNA quadruplexes and provides a model for a conserved antiviral target in Orthoflavivirus genomes.

60 APPLIED LIFE SCIENCES↗

Structural basis of tRNA recognition by the widespread OB fold

The widespread oligonucleotide/oligosaccharide-binding (OB)-fold recognizes diverse substrates from sugars to nucleic acids and proteins, and plays key roles in genome maintenance, transcription, translation, and tRNA metabolism. OB-containing bacterial Trbp and yeast Arc1p proteins are thought to recognize the tRNA elbow or anticodon regions. Here we report a 2.6 Å co-crystal structure of Aquifex aeolicus Trbp111 bound to tRNA Ile , which reveals that Trbp recognizes tRNAs solely by capturing their 3' ends. Structural, mutational, and biophysical analyses show that the Trbp/EMAPII-like OB fold precisely recognizes the single-stranded structure, 3' terminal location, and specific sequence of the 3' CA dinucleotide — a universal feature of mature tRNAs. Arc1p supplements its OB – tRNA 3' end interaction with additional contacts that involve an adjacent basic region and the tRNA body. This study uncovers a previously unrecognized mode of tRNA recognition by an ancient protein fold, and provides insights into protein-mediated tRNA aminoacylation, folding, localization, trafficking, and piracy.

59 BASIC BIOLOGICAL SCIENCES↗

Dynamic basis of supercoiling-dependent DNA interrogation by Cas12a via R-loop intermediates

The sequence specificity and programmability of DNA binding and cleavage have enabled widespread applications of CRISPR-Cas12a in genetic engineering. As an RNA-guided CRISPR endonuclease, Cas12a engages a 20-base pair (bp) DNA segment by forming a three-stranded R-loop structure in which the guide RNA hybridizes to the DNA target. Here we use single-molecule torque spectroscopy to investigate the dynamics and mechanics of R-loop formation of two widely used Cas12a orthologs at base-pair resolution. We directly observe kinetic intermediates corresponding to a ~5 bp initial RNA-DNA hybridization and a ~17 bp intermediate preceding R-loop completion, followed by transient DNA unwinding that extends beyond the 20 bp R-loop. The complex multistate landscape of R-loop formation is ortholog-dependent and shaped by target sequence, mismatches, and DNA supercoiling. A four-state kinetic model captures essential features of Cas12a R-loop dynamics and provides a biophysical framework for understanding Cas12a activity and specificity.

59 BASIC BIOLOGICAL SCIENCES↗

Cell surface crowding is a tunable energetic barrier to cell-cell fusion

Abstract Cell-cell fusion is fundamental to processes such as muscle formation and viral infection. An essential step in fusion is close membrane apposition, but cell membranes are crowded with proteins, glycoproteins, and glycolipids, which must be cleared before a fusion pore can be nucleated. Here, we find that cell surface crowding reduces fusogenicity independent of how fusion is driven. We estimate that crowding presents an energetic barrier to membrane apposition on the scale of ~$${100k}_{{\mbox{B}}}T$$ 100 k B T , greater than that of bare membrane fusion alone. We show that increasing cell surface crowding reduces fusion efficiency of PEG-mediated and fusogen-mediated cell-cell fusion, as well as synthetic membranes under force. Interestingly, we find that differentiating myoblasts naturally decrease their surface crowding prior to fusion. In this work, we show that cell surface crowding presents an underappreciated biophysical barrier that may be tuned developmentally and could be targeted externally to control tissue-specific cell-cell fusion.

Science & Technology - Other Topics↗

A single diiron enzyme catalyses the oxidative rearrangement of tryptophan to indole nitrile

Abstract Nitriles are uncommon in nature and are typically constructed from oximes through the oxidative decarboxylation of amino acid substrates or from the derivatization of carboxylic acids. Here we report a third nitrile biosynthesis strategy featuring the cyanobacterial nitrile synthase AetD. During the biosynthesis of the eagle-killing neurotoxin, aetokthonotoxin, AetD transforms the 2-aminopropionate portion of 5,7-dibromo-l-tryptophan to a nitrile. Employing a combination of structural, biochemical and biophysical techniques, we characterized AetD as a non-haem diiron enzyme that belongs to the emerging haem-oxygenase-like dimetal oxidase superfamily. High-resolution crystal structures of AetD together with the identification of catalytically relevant products provide mechanistic insights into how AetD affords this unique transformation, which we propose proceeds via an aziridine intermediate. Our work presents a unique template for nitrile biogenesis and portrays a substrate binding and metallocofactor assembly mechanism that may be shared among other haem-oxygenase-like dimetal oxidase enzymes.

Chemistry↗

Complex water networks visualized by cryogenic electron microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water. Here we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule: the Tetrahymena ribozyme. By using segmentation-guided water and ion modelling (SWIM), an approach combining resolvability and chemical parameters, we automatically modelled and cross-validated water molecules and Mg 2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics, supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modelling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and molecular dynamics simulations.

59 BASIC BIOLOGICAL SCIENCES↗

Small-molecule modulation of β-arrestins

β-Arrestins are multifunctional regulators of G-protein-coupled receptor (GPCR) signalling and orchestrate diverse downstream signalling events and physiological responses across the GPCR superfamily. Although GPCR pharmacology has advanced to target orthosteric and allosteric sites, as well as G proteins and GPCR kinases, direct chemical tools to modulate β-arrestin activities have remained conspicuously absent. Here we report the identification of small-molecule inhibitors that selectively target β-arrestins and delineate their mechanism of action through integrated pharmacological, biochemical, biophysical and structural analyses. These inhibitors disrupt β-arrestin engagement with agonist-activated GPCRs, impairing desensitization, internalization and β-arrestin-dependent physiological functions while sparing G protein–receptor coupling. Cryo-electron microscopy, molecular dynamics simulations and structure-guided mutagenesis reveal that one modulator, Cmpd-5, engages a pocket within the central crest of β-arrestin1 formed by the middle, C and lariat loops, a critical receptor-binding interface, stabilizing a distinct conformation that is incompatible with full β-arrestin–receptor engagement. Together, these findings establish a mechanistic framework for β-arrestin modulation, reveal a novel allosteric site for structure-based drug design, and open new avenues for transducer-targeted, pathway-specific GPCR therapeutic agents.

Kahsai, Alem W. [Duke University, Durham, NC (Unit↗

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗

A ligand discovery toolbox for the WWE domain family of human E3 ligases

The WWE domain is a relatively under-researched domain found in twelve human proteins and characterized by a conserved tryptophan-tryptophan-glutamate (WWE) sequence motif. Six of these WWE domain-containing proteins also contain domains with E3 ubiquitin ligase activity. The general recognition of poly-ADP-ribosylated substrates by WWE domains suggests a potential avenue for development of Proteolysis-Targeting Chimeras (PROTACs). Here, we present novel crystal structures of the HUWE1, TRIP12, and DTX1 WWE domains in complex with PAR building blocks and their analogs, thus enabling a comprehensive analysis of the PAR binding site structural diversity. Furthermore, we introduce a versatile toolbox of biophysical and biochemical assays for the discovery and characterization of novel WWE domain binders, including fluorescence polarization-based PAR binding and displacement assays, 15 N-NMR-based binding affinity assays and 19 F-NMR-based competition assays. Through these assays, we have characterized the binding of monomeric iso -ADP-ribose ( iso -ADPr) and its nucleotide analogs with the aforementioned WWE proteins. Finally, we have utilized the assay toolbox to screen a small molecule fragment library leading to the successful discovery of novel ligands targeting the HUWE1 WWE domain.

59 BASIC BIOLOGICAL SCIENCES↗

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗

Intermediate time sub-diffusion and stress relaxation in ring polymer melts

The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, in this work, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/N e . Rather, we find that the crossover degree of polymerization, N D , based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of N D / N . A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.

Anomalous diffusion↗

Cooling outweighs warming across phenological transitions in the Northern Hemisphere

Vegetation phenology, i.e., seasonal biological events such as leaf-out and leaf-fall, regulates local climate through biophysical processes like evapotranspiration (ET) and albedo. However, the net surface temperature impact of these processes—whether ET cooling or albedo-induced warming predominates—and how the dominance changes across phenological transitions and regions remains poorly understood. Here, we investigated the effects of vegetation foliage on daytime land surface temperature (LST) following six phenological transitions, spanning from the start of season to end of season, in deciduous and mixed forests across the mid- to high-latitude Northern Hemisphere during 2013–2021 using multiple satellite products and ground observations. We quantified vegetation effect as the difference between observed LST and LST estimates from the Annual Temperature Cycle (ATC) model, representing a no-foliage scenario. We found that vegetation-induced cooling consistently outweighs warming following all phenological transitions except for the end of the season. Cooling intensity increased with vegetation greenness, ranging from 1.0 ± 0.5 °C (mean ± 0.15 SD) in 59% of forests after the start of the season (SOS) to 6.1 ± 0.8 °C in 89% of forests following the onset of maturity, before declining toward the end of the season. Over half of the regions experiencing cooling showed intensification of surface cooling with climate warming, suggesting an amplified vegetation-mediated cooling under future climate change. The findings provide a more precise understanding of the role of vegetation in modulating climate at the intraseasonal scale, highlighting the importance of integrating phenological impacts into climate adaptation strategies and Earth system modeling.

albedo↗

Limits to forests-based mitigation in integrated assessment modelling: global potentials and impacts under constraining factors

Forests-based measures such as afforestation/reforestation (A/R) and reducing deforestation (RDF) are considered promising options to mitigate climate change, yet their mitigation potentials are limited by economic and biophysical factors that are largely uncertain. The range of mitigation potential estimates from integrated assessment models raises concerns about the capacity of land systems to provide realistic, cost-effective and permanent land-based mitigation. We use the Global Change Analysis Model to quantify the economic mitigation potential of forests-based measures by simulating a climate policy including a tax on greenhouse gas emissions from agriculture, forestry, and other land uses. In addition, we assess how constraining unused arable land (UAL) availability, forestland expansion rates, and global bioenergy demand may influence the forests-based mitigation potential by simulating scenarios with alternative combinations of constraints. Results show that the average forests-based mitigation potential in 2020–2050 increases from 738 MtCO 2 .yr -1 through a forestland increase of 86 Mha in the fully constrained scenario to 1394 MtCO 2 .yr -1 through a forestland increase of 146 Mha when all constraints are relaxed. Regional potentials in terms of A/R and RDF differ strongly between scenarios: unconstrained forest expansion rates mostly increase A/R potentials in northern regions (e.g., +120 MtCO 2 .yr -1 in North America); while unconstrained UAL conversion and low bioenergy demand mostly increase RDF potentials in tropical regions (e.g., +76 and +68 MtCO 2 .yr -1 in Southeast Asia, respectively). This study shows that forests-based mitigation is limited by many factors that constrain the rates of land use change across regions. These factors, often overlooked in modelling exercises, should be carefully addressed for understanding the role of forests in global climate mitigation and defining pledges towards the Paris Agreement.

54 ENVIRONMENTAL SCIENCES↗

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Lai, Jhih-Siang (ORCID:0000000156775890)↗

Interaction of N-methylmesoporphyrin IX with a hybrid left-/right-handed G-quadruplex motif from the promoter of the SLC2A1 gene

Abstract Left-handed G-quadruplexes (LHG4s) belong to a class of recently discovered noncanonical DNA structures under the larger umbrella of G-quadruplex DNAs (G4s). The biological relevance of these structures and their ability to be targeted with classical G4 ligands is underexplored. Here, we explore whether the putative LHG4 DNA sequence from the SLC2A1 oncogene promoter maintains its left-handed characteristics upon addition of nucleotides in the 5′- and 3′-direction from its genomic context. We also investigate whether this sequence interacts with a well-established G4 binder, N-methylmesoporphyrin IX (NMM). We employed biophysical and X-ray structural studies to address these questions. Our results indicate that the sequence d[G(TGG)3TGA(TGG)4] (termed here as SLC) adopts a two-subunit, four-tetrad hybrid left-/right-handed G4 (LH/RHG4) topology. Addition of 5′-G or 5′-GG abolishes the left-handed fold in one subunit, while the addition of 3′-C or 3′-CA maintains the original fold. X-ray crystal structure analyses show that SLC maintains the same hybrid LH/RHG4 fold in the solid state and that NMM stacks onto the right-handed subunit of SLC. NMM binds to SLC with a 1:1 stoichiometry and a moderate-to-tight binding constant of 15 μM−1. This work deepens our understanding of LHG4 structures and their binding with traditional G4 ligands.

Seth, Paul↗