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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. Finally, a synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Protein Data Bank (PDB): Fifty-three years young and having a transformative impact on science and society

This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.

bioinformatics↗
DOE OSTI2023

Astrovirology: how viruses enhance our understanding of life in the Universe

Abstract Viruses are the most numerically abundant biological entities on Earth. As ubiquitous replicators of molecular information and agents of community change, viruses have potent effects on the life on Earth, and may play a critical role in human spaceflight, for life-detection missions to other planetary bodies and planetary protection. However, major knowledge gaps constrain our understanding of the Earth's virosphere: (1) the role viruses play in biogeochemical cycles, (2) the origin(s) of viruses and (3) the involvement of viruses in the evolution, distribution and persistence of life. As viruses are the only replicators that span all known types of nucleic acids, an expanded experimental and theoretical toolbox built for Earth's viruses will be pivotal for detecting and understanding life on Earth and beyond. Only by filling in these knowledge and technical gaps we will obtain an inclusive assessment of how to distinguish and detect life on other planetary surfaces. Meanwhile, space exploration requires life-support systems for the needs of humans, plants and their microbial inhabitants. Viral effects on microbes and plants are essential for Earth's biosphere and human health, but virus–host interactions in spaceflight are poorly understood. Viral relationships with their hosts respond to environmental changes in complex ways which are difficult to predict by extrapolating from Earth-based proxies. These relationships should be studied in space to fully understand how spaceflight will modulate viral impacts on human health and life-support systems, including microbiomes. In this review, we address key questions that must be examined to incorporate viruses into Earth system models, life-support systems and life detection. Tackling these questions will benefit our efforts to develop planetary protection protocols and further our understanding of viruses in astrobiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structural Characterization and Ligand-Induced Conformational Changes of SenB, a Se-Glycosyltransferase Involved in Selenoneine Biosynthesis

Selenium (Se) is an essential micronutrient that is found naturally in proteins, nucleic acids, and natural products. Unlike selenoproteins and selenonucleic acids, little is known about the structures of biosynthetic enzymes that incorporate Se into small molecules. Here, we report the X-ray crystal structure of SenB, the first known Se-glycosyltransferase that was recently found to be involved in the biosynthesis of the Se-containing metabolite selenoneine. SenB catalyzes C–Se bond formation using selenophosphate and an activated uridine diphosphate sugar as a Se and glycosyl donor, respectively, making it the first known selenosugar synthase and one of only four bona fide C–Se bond-forming enzymes discovered to date. Our crystal structure, determined to 2.25 Å resolution, reveals that SenB is a type B glycosyltransferase, displaying the prototypical fold with two globular Rossmann-like domains and a catalytic interdomain cleft. By employing complementary structural biology techniques, we find that SenB undergoes both local and global substrate-induced conformational changes, demonstrating a significant increase in α-helicity and a transition to a more compact conformation. Our results provide the first structure of SenB and set the stage for further biochemical characterization in the future.

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

Atomic Force Microscopy-Based Force Spectroscopy and Multiparametric Imaging of Biomolecular and Cellular Systems

During the last three decades, a series of key technological improvements turned atomic force microscopy (AFM) into a nanoscopic laboratory to directly observe and chemically characterize molecular and cell biological systems under physiological conditions. In this work, we review key technological improvements that have established AFM as an analytical tool to observe and quantify native biological systems from the micro- to the nanoscale. Native biological systems include living tissues, cells, and cellular components such as single or complexed proteins, nucleic acids, lipids, or sugars. We showcase the procedures to customize nanoscopic chemical laboratories by functionalizing AFM tips and outline the advantages and limitations in applying different AFM modes to chemically image, sense, and manipulate biosystems at (sub)nanometer spatial and millisecond temporal resolution. We further discuss theoretical approaches to extract the kinetic and thermodynamic parameters of specific biomolecular interactions detected by AFM for single bonds and extend the discussion to multiple bonds. Finally, we highlight the potential of combining AFM with optical microscopy and spectroscopy to address the full complexity of biological systems and to tackle fundamental challenges in life sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein–ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA–small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand–protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein–target docking illustrates a need for further methods refinement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Folding Coarse-Grained Oligomer Models with PyRosetta

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta’s ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. Here, we show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling the Ultrafast Electron Attachment Dynamics of Solvated Uracil

Electron attachment to DNA by low energy electrons can lead to DNA damage, so a fundamental understanding of how electrons interact with the components of nucleic acids in solution is an open challenge. In solution, low energy electrons can generate presolvated electrons, e pre – , which are efficiently scavanged by pyrimidine nucleobases to form transient negative ions, able to relax to either stable valence bound anions or undergo dissociative electron detachment or transfer to other parts of DNA/RNA leading to strand breakages. In order to understand the initial electron attachment dynamics, this paper presents a joint molecular dynamics and high-level electronic structure study into the behavior of the electronic states of the solvated uracil anion. Both the valence π* and nonvalence e pre – states of the solvated uracil system are studied, and the effect of the solvent environment and the geometric structure of the uracil core are uncoupled to gain insight into the physical origin of the stabilization of the solvated uracil anion. As a result, solvent reorganization is found to play a dominant role followed by relaxation of the uracil core.

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

Metagenome-Guided Proteomic Quantification of Reductive Dehalogenases in the Dehalococcoides mccartyi -Containing Consortium SDC-9

At groundwater sites contaminated with chlorinated ethenes, fermentable substrates are often added to promote reductive dehalogenation by indigenous or augmented microorganisms. Contemporary bioremediation performance monitoring relies on nucleic acid biomarkers of key organohalide-respiring bacteria, such as Dehalococcoides mccartyi (Dhc). In this work, metagenome sequencing of the commercial, Dhc-containing consortium, SDC-9, identified 12 reductive dehalogenase (RDase) genes, including pceA (two copies), vcrA, and tceA, and allowed for specific detection and quantification of RDase peptides using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Shotgun (i.e., untargeted) proteomics applied to the SDC-9 consortium grown with tetrachloroethene (PCE) and lactate identified 143 RDase peptides, and 36 distinct peptides that covered greater than 99% of the protein-coding sequences of the PceA, TceA, and VcrA RDases. Quantification of RDase peptides using multiple reaction monitoring (MRM) assays with 13 C-/ 15 N-labeled peptides determined 1.8 × 10 3 TceA and 1.2 × 10 2 VcrA RDase molecules per Dhc cell. Ultimately, the MRM mass spectrometry approach allowed for sensitive detection and accurate quantification of relevant Dhc RDases and has potential utility in bioremediation monitoring regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Bottom-Up Simulation, Reconstruction, and Quantification of Macromolecule Sequences from Experimental Polymerizations

Motivated by the canonical sequence–structure–function paradigm, tools to characterize chemical patterning in natural biomacromolecules, from proteins to nucleic acids, have grown exponentially in recent years. However, analogous strategies for synthetic macromolecules remain in nascent stages, complicated by sequence polydispersity and analytical limitations. To address this, we have developed a comprehensive and open-source Python package, PRISM (polymer rate insights and sequence modeling), an end-to-end workflow that provides a path from experimental kinetics measurements to quantitative and qualitative metrics for describing chemical patterning in stochastic polymers. First, a numerical integration strategy was constructed to simulate and fit experimental data from reversible addition–fragmentation chain transfer (RAFT) polymerization kinetics, enabling the facile estimation of relevant reactivity ratios. These ratios were then used in a mechanism-specific stochastic kinetic simulation strategy to simulate sequence ensembles corresponding to model systems spanning experimental copolymers, classes of statistical polymers (e.g., alternating, block, and gradient), and multiblock copolymers. Lastly, inspired by sequence homology metrics from bioinformatics, we introduce visualization strategies and quantitative metrics to facilitate comparisons of different sequence ensembles. As the sequence–structure–function paradigm becomes increasingly central in de novo design of synthetic macromolecules, this toolkit provides a first step toward accurate and representative sequence description and featurization.

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

Tre-DST: A Drug Susceptibility Test for Mycobacterium tuberculosis Using Solvatochromic Trehalose Probes

In 2024, an estimated 10 million people developed Tuberculosis (TB), nearly half a million of whom were infected with drug-resistant tuberculosis (DR-TB). Early detection of infection and drug resistance enables rapid engagement in effective care. Bacterial culture and nucleic acid testing remain the primary diagnostic methods, with smear microscopy being phased out. However, these methods present significant limitations for diagnosing drug resistance, such as lengthy time-to-result for phenotypic tests, as well as the need for prior knowledge of resistance mutations and prohibitive cost for molecular tests. To address this, we developed a rapid phenotypic TB drug susceptibility test, termed Tre-DST, based on novel metabolically incorporated trehalose probes, which specifically detect live mycobacteria. We used the nonpathogenic Mycobacterium smegmatis and the virulence-attenuated Mycobacterium tuberculosis (Mtb) H37Ra or auxotrophic Mtb to demonstrate a strong correlation between cost-effective plate reader results and flow cytometry data, suggesting that the plate reader is a suitable fluorescence detector for Tre-DST. We determined that adding a 1-week incubation step allowed Mtb samples originally seeded at 10 4 CFU/mL to become detectable, over 2 weeks earlier than colony-forming unit analysis. We found that Tre-DST reports on drug susceptibility in a drug-agnostic manner, demonstrating loss of fluorescence with frontline TB drugs as well as the newer drug bedaquiline. Tre-DST distinguished RIF- and INH-resistant auxotrophs from susceptible controls and accurately reported the resistance activity. Ultimately, because Tre-DST is agnostic to mechanisms of drug resistance, this assay is likely compatible with all WHO-recommended and future DR-TB drugs as a diagnostic in reference laboratories.

diagnostics↗
DOE OSTI2025

Nanotubes Growth by Self-Assembly of DNA Strands at Room Temperature

Artificial biomolecular nanotubes are a promising approach to building materials mimicking the capacity of the cellular cytoskeleton to grow and self-organize dynamically. Nucleic acid nanotechnology has demonstrated a variety of self-assembling nanotubes with programmable, robust features and morphological similarities to actual cytoskeleton components. However, their production typically requires thermal annealing, which not only poses a general constraint on their potential applications but is also incompatible with physiological conditions. Here, we demonstrate that DNA nanotubes can self-assemble from a simple mixture of five short DNA strands at constant room temperature, growing for extended periods of time in bulk conditions as well as under confinement. Assembly is achieved using a monovalent salt buffer, which ensures a faithful nanoscale arrangement and avoids nanotube aggregation. We observe the formation of individual nanotubes up to 20 days with a diameter of 22 ± 4 nm and length of several tens of micrometers. We finally encapsulate the strands in microsized compartments, such as water-in-oil microdroplets and giant unilamellar vesicles serving as simple cell models. Notably, nanotubes not only isothermally self-assemble directly inside the microcompartments but also self-organize into dynamic higher-order structures resembling rings and dynamic networks. Our study provides an advantageous method for in situ assembly of programmable biomolecular scaffolds and materials using synthetic DNA strands without requirements of thermal treatment.

Chemistry↗
DOE OSTI2020

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PNA-Assisted DNAzymes to Cleave Double-Stranded DNA for Genetic Engineering with High Sequence Fidelity

DNAzymes have been widely used in many sensing and imaging applications but have rarely been used for genetic 5 engineering since their discovery in 1994, because their substrate scope is mostly limited to single-stranded DNA or RNA, whereas 6 genetic information is stored mostly in double-stranded DNA (dsDNA). To overcome this major limitation, in this paper we report 7 peptide nucleic acid (PNA)-assisted double-stranded DNA nicking by DNAzymes (PANDA) as the first example to expand 8 DNAzyme activity toward dsDNA. We show that PANDA is programmable in efficiently nicking or causing double strand breaks on 9 target dsDNA, which mimics protein nucleases and can act as restriction enzymes in molecular cloning. In addition to being much 10 smaller than protein enzymes, PANDA has a higher sequence fidelity compared with CRISPR/Cas under the condition we tested, 11 demonstrating its potential as a novel alternative tool for genetic engineering and other biochemical applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Directing Nanoparticle Organization in Response to Diverse Chemical Inputs

Signaling cascades are crucial for transducing stimuli in biological systems, enabling multiple stimuli to regulate a downstream target with precisely controlled timing and amplifying signals through a series of intermediary reactions. Developing a robust signaling system with such capabilities would be pivotal for programming complex behaviors in synthetic DNA-based molecular devices. However, although “software” such as nucleic acid circuits could potentially be harnessed to relay signals to DNA-based nanostructure hardware, such explorations have been limited. Here, in this study, we develop a platform for transducing a variety of stimuli via messenger-mediated reactions to regulate the release and reloading of gold nanoparticles (AuNPs) in a 3D DNA framework. In the first step, an in vitro transcription circuit is engineered to sense and amplify chemical stimuli, including arbitrary DNA sequences and proteins, producing RNA. In the second step, the RNA releases the DNA-coated AuNPs from the DNA framework via a strand displacement reaction. AuNP reloading is controlled by a separate step driven by degradation of the RNA. Our platform holds promise for applications requiring dynamic multiagent control over DNA-based devices, offering a versatile tool for advanced molecular device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sequence-selective dynamic covalent assembly of information-bearing oligomers

Relatively robust dynamic covalent interactions have been employed extensively to mediate molecular self-assembly reactions; however, these assembly processes often do not converge to a thermodynamic equilibrium, instead yielding mixtures of kinetically-trapped species. Here, we report a dynamic covalent self-assembly process that mitigates kinetic trapping such that multiple unique oligomers bearing covalently coreactive pendant groups are able to undergo simultaneous, sequence-selective hybridization with their complementary strands to afford biomimetic, in-registry molecular ladders with covalent rungs. Analogous to the thermal cycling commonly employed for nucleic acid melting and annealing, this is achieved by raising and lowering the concentration of a multi-role reagent to effect quantitative dissociation and subsequently catalyze covalent bond rearrangement, affording selective assembly of the oligomeric sequences. The hybridization specificity afforded by this process further enabled information encoded in oligomers to be retrieved through selective hybridization with complementary, mass-labeled sequences.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R 2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R 2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Dynamic self-assembly of compartmentalized DNA nanotubes

Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗