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Results for “molecular activity”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The molecular basis of pyrazinamide activity on Mycobacterium tuberculosis PanD

Pyrazinamide has been a mainstay in the multidrug regimens used to treat tuberculosis. It is active against the persistent, non-replicating mycobacteria responsible for the protracted therapy required to cure tuberculosis. Pyrazinamide is a pro-drug that is converted into pyrazinoic acid (POA) by pyrazinamidase, however, the exact target of the drug has been difficult to determine. Here we show the enzyme PanD binds POA in its active site in a manner consistent with competitive inhibition. The active site is not directly accessible to the inhibitor, suggesting the protein must undergo a conformational change to bind the inhibitor. This is consistent with the slow binding kinetics we determined for POA. Drug-resistant mutations cluster near loops that lay on top of the active site. These resistant mutants show reduced affinity and residence time of POA consistent with a model where resistance occurs by destabilizing the closed conformation of the active site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structure of the phosphoinositide 3-kinase (PI3K) p110γ-p101 complex reveals molecular mechanism of GPCR activation

The class IB phosphoinositide 3-kinase (PI3K), PI3Kγ, is a master regulator of immune cell function and a promising drug target for both cancer and inflammatory diseases. Critical to PI3Kγ function is the association of the p110γ catalytic subunit to either a p101 or p84 regulatory subunit, which mediates activation by G protein–coupled receptors. Here, we report the cryo–electron microscopy structure of a heterodimeric PI3Kγ complex, p110γ-p101. This structure reveals a unique assembly of catalytic and regulatory subunits that is distinct from other class I PI3K complexes. p101 mediates activation through its Gβγ-binding domain, recruiting the heterodimer to the membrane and allowing for engagement of a secondary Gβγ-binding site in p110γ. Mutations at the p110γ-p101 and p110γ–adaptor binding domain interfaces enhanced Gβγ activation. A nanobody that specifically binds to the p101-Gβγ interface blocks activation, providing a novel tool to study and target p110γ-p101–specific signaling events in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗
DOE OSTI2024

Assessing electrogenetic activation via a network model of biological signal propagation

Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control. Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia. Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks. Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.

Chun, Kayla↗
DOE OSTI2020

Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease

There is an urgent need to repurpose drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent computational-experimental screenings have identified several existing drugs that could serve as effective inhibitors of the virus’ main protease, M pro , which is involved in gene expression and replication. Among these, ebselen (2-phenyl-1,2-benzoselenazol-3-one) appears to be particularly promising. Here, we examine, at a molecular level, the potential of ebselen to decrease Mpro activity. We find that it exhibits a distinct affinity for the catalytic region. Our results reveal a higher-affinity, previously unknown binding site localized between the II and III domains of the protein. A detailed strain analysis indicates that, on such a site, ebselen exerts a pronounced allosteric effect that regulates catalytic site access through surface-loop interactions, thereby inducing a reconfiguration of water hotspots. Together, these findings highlight the promise of ebselen as a repurposed drug against SARS-CoV-2.

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

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗
DOE OSTI2020

Local Environment Affects the Activity of Enzymes on a 3D Molecular Scaffold

The ability to coordinate and confine enzymes presents an opportunity to affect their performance and to create chemically active materials. Recent studies show that polymers and biopolymers can be used to scaffold enzymes, and that can lead to the modulated biocatalytic efficiency. In this work, we investigated the role of microenvironments on enzyme activity using a well-defined molecular scaffold. An enzyme, glucose oxidase (GO x ), was positioned at different locations of a three-dimensional (3D) octahedral DNA scaffold (OS), allowing the enzyme’s polyanionic environments to be altered. Using electrical sensing, based on a bipolar junction transistor, we measured directly and in real-time the enzyme’s proton generation at these different microenvironments. We found a 200% enhancement of immobilized enzyme over free GOx and about a 30% increase in catalytic rates when the enzyme was moved on the same molecular scaffold to a microenvironment with a higher local concentration of polyanions, which suggests a role of local pH on the enzymatic activity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Surface molecular pump enables ultrahigh catalyst activity

The performance of electrocatalysts is critical for renewable energy technologies. While the electrocatalytic activity can be modulated through structural and compositional engineering following the Sabatier principle, the insufficiently explored catalyst-electrolyte interface is promising to promote microkinetic processes such as physisorption and desorption. By combining experimental designs and molecular dynamics simulations with explicit solvent in high accuracy, we demonstrated that dimethylformamide can work as an effective surface molecular pump to facilitate the entrapment of oxygen and outflux of water. Dimethylformamide disrupts the interfacial network of hydrogen bonds, leading to enhanced activity of the oxygen reduction reaction by a factor of 2 to 3. This strategy works generally for platinum-alloy catalysts, and we introduce an optimal model PtCuNi catalyst with an unprecedented specific activity of 21.8 ± 2.1 mA/cm 2 at 0.9 V versus the reversible hydrogen electrode, nearly double the previous record, and an ultrahigh mass activity of 10.7 ± 1.1 A/mg Pt .

Science & Technology - Other Topics↗
DOE OSTI2020

Single-Facet Dominant Anatase TiO 2 (101) and (001) Model Catalysts to Elucidate the Active Sites for Alkanol Dehydration

Alkanol dehydration on Lewis acid-base pairs of transition metal oxide catalysts is a reaction of importance in oxygen removal from biomass-derived feedstocks and their conversion to chemicals in general. However, catalysts with a high degree of structural heterogeneity, such as commercial TiO 2 powders, are not well-suited to establish rigorous structure-function relationships at an atomic level. Here, we provide compelling evidence for the effects of surface orientation of TiO 2 catalyst on elimination reactions of alcohols. Two anatase titania model catalysts, with preferential exposure of (101) and (001) facets, were synthesized and studied for 2-propanol dehydration using kinetic, isotopic, microscopic, and spectroscopic measurements, coupled with DFT calculations. Surface Lewis acid sites were found to be active for 2-propanol dehydration and (101) facets are more reactive than (001) facets under the reaction conditions studied. On both anatase surfaces, 2-propanol was found to dehydrate via concerted E2 elimination pathways, but with different initial states and thus also different intrinsic activation barriers. Molecular 2-propanol dehydration dominates on TiO2 (101) while on TiO 2 (001), 2-propanol simultaneously converts to more stable 2-propoxide before dehydration, which then requires higher activation energies for E2 elimination.

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

Electron donation of non-oxide supports boosts O 2 activation on nano-platinum catalysts

Activation of O 2 is a critical step in heterogeneous catalytic oxidation. Here, the concept of increased electron donors induced by nitrogen vacancy is adopted to propose an efficient strategy to develop highly active and stable catalysts for molecular O 2 activation. Carbon nitride with nitrogen vacancies is prepared to serve as a support as well as electron sink to construct a synergistic catalyst with Pt nanoparticles. Extensive characterizations combined with the first-principles calculations reveal that nitrogen vacancies with excess electrons could effectively stabilize metallic Pt nanoparticles by strong p-d coupling. The Pt atoms and the dangling carbon atoms surround the vacancy can synergistically donate electrons to the antibonding orbital of the adsorbed O 2 . This synergistic catalyst shows great enhancement of catalytic performance and durability in toluene oxidation. The introduction of electron-rich non-oxide substrate is an innovative strategy to develop active Pt-based oxidation catalysts, which could be conceivably extended to a variety of metal-based catalysts for catalytic oxidation.

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

w19_OMEC Scientific Highlight [Slides]

Scientific Achievement: Revealed the effect of graphite substrate on H binding / hydrogen evolution reaction (HER) activity of organic molecular electrocatalysts (OMECs); and demonstrated the effect of molecular modification on calculated HER activity descriptor. Significance and Impact: Understanding of HER processes is important for discovering low-cost OMECs for hydrogen production and energy application; design of OMEC structures remains challenging due to the unexplored redox properties of organic molecules; and density functional theory (DFT) has been proposed to provide needed insights into HER activity for improved materials design. Research Details: DFT calculations on the binding energy of HER intermediates are used for predicting reduction potentials and and calculated H adsorption energy as activity descriptor; and LANL Institutional Computing (IC) resources are essential for successful project execution given the size of structures involved in modeling molecule-support interaction.

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

High-resolution CARMA Observation of Molecular Gas in the North America and Pelican Nebulae

In this paper, we present the first results from a CARMA high-resolution {sup 12}CO(1-0), {sup 13}CO(1-0), and C{sup 18}O(1-0) molecular line survey of the North America and Pelican (NAP) Nebulae. CARMA observations have been combined with single-dish data from the Purple Mountain 13.7 m telescope, to add short spacings and to produce high-dynamic-range images. We find that the molecular gas is predominantly shaped by the W80 H ii bubble, driven by an O star. Several bright rims noted in the observation are probably remnant molecular clouds, heated and stripped by the massive star. Matching these rims in molecular lines and optical images, we construct a model of the three-dimensional structure of the NAP complex. Two groups of molecular clumps/filaments are on the near side of the bubble: one is being pushed toward us, whereas the other is moving toward the bubble. Another group is on the far side of the bubble, and moving away. The young stellar objects in the Gulf region reside in three different clusters, each hosted by a cloud from one of the three molecular clump groups. Although all gas content in the NAP is impacted by feedback from the central O star, some regions show no signs of star formation, while other areas clearly exhibit star formation activity. Additional molecular gas being carved by feedback includes cometary structures in the Pelican Head region, and the boomerang features at the boundary of the Gulf region. The results show that the NAP complex is an ideal place for the study of feedback effects on star formation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

De Novo Design of Proteins That Bind Naphthalenediimides, Powerful Photooxidants with Tunable Photophysical Properties

De novo protein design provides a framework to test our understanding of protein function and build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small-molecule reactive species. Because excited-state dynamics are influenced by the dynamics and hydration of a photooxidant’s environment, it was important to not only design a binding site but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP (NDI Binding Protein), that held a naphthalenediimide (NDI), a powerful photooxidant, in a programmable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein using ultrafast pump–probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein labeling.

carbonyls↗
DOE OSTI2021

Visible light enables catalytic formation of weak chemical bonds with molecular hydrogen

The synthesis of weak chemical bonds at or near thermodynamic potential is a fundamental challenge in chemistry, with applications ranging from catalysis to biology to energy science. Proton-coupled electron transfer using molecular hydrogen is an attractive strategy for synthesizing weak element–hydrogen bonds, but the intrinsic thermodynamics presents a challenge for reactivity. Here we describe the direct photocatalytic synthesis of extremely weak element–hydrogen bonds of metal amido and metal imido complexes, as well as organic compounds with bond dissociation free energies as low as 31 kcal mol –1 . Key to this approach is the bifunctional behaviour of the chromophoric iridium hydride photocatalyst. Activation of molecular hydrogen occurs in the ground state and the resulting iridium hydride harvests visible light to enable spontaneous formation of weak chemical bonds near thermodynamic potential with no by-products. In conclusion, photophysical and mechanistic studies corroborate radical-based reaction pathways and highlight the uniqueness of this photodriven approach in promoting new catalytic chemistr

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

A new method for measuring the 3D turbulent velocity dispersion of molecular clouds

ABSTRACT The structure and star formation activity of a molecular cloud are fundamentally linked to its internal turbulence. However, accurately measuring the turbulent velocity dispersion is challenging due to projection effects and observational limitations, such as telescope resolution, particularly for clouds that include non-turbulent motions, such as large-scale rotation. Here, we develop a new method to recover the 3D turbulent velocity dispersion (σv,3D) from position–position–velocity (PPV) data. We simulate a rotating, turbulent, collapsing molecular cloud, and compare its intrinsic σv,3D with three different measures of the velocity dispersion accessible in PPV space: (1) the spatial mean of the 2nd-moment map, σi, (2) the standard deviation of the gradient/rotation-corrected 1st-moment map, σ(c − grad), and (3) a combination of (1) and (2), called the ‘gradient-corrected parent velocity dispersion’, $\sigma _{\mathrm{(p}-\mathrm{grad)}}=(\sigma _{\mathrm{i}}^2+\sigma _{(\mathrm{c}-\mathrm{grad)}}^2)^{1/2}$. We show that the gradient correction is crucial in order to recover purely turbulent motions of the cloud, independent of the orientation of the cloud with respect to the line of sight. We find that with a suitable correction factor and appropriate filters applied to the moment maps, all three statistics can be used to recover σv,3D, with method 3 being the most robust and reliable. We determine the correction factor as a function of the telescope beam size for different levels of cloud rotation, and find that for a beam full width at half-maximum f and cloud radius R, the 3D turbulent velocity dispersion can best be recovered from the gradient-corrected parent velocity dispersion via $\sigma _{v,\mathrm{3D}}= \left[(-0.29\pm 0.26)\, f/R + 1.93 \pm 0.15\right] \sigma _{\mathrm{(p}-\mathrm{grad)}}$ for f/R < 1, independent of the level of cloud rotation or LOS orientation.

Stewart, Madeleine↗
DOE OSTI2022

Atomically Thin, Ionic–Covalent Organic Nanosheets for Stable, High–Performance Carbon Dioxide Electroreduction

The incorporation of charged functional groups is effective to modulate the activity of molecular complexes for the CO 2 reduction reaction (CO 2 RR), yet long-term heterogeneous electrolysis is often hampered by catalyst leaching. Herein, an electrocatalyst of atomically thin, cobalt-porphyrin-based, ionic–covalent organic nanosheets (CoTAP-iCONs) is synthesized via a post-synthetic modification strategy for high-performance CO 2 -to-CO conversion. The cationic quaternary ammonium groups not only enable the formation of monolayer nanosheets due to steric hindrance and electrostatic repulsion, but also facilitate the formation of a *COOH intermediate, as suggested by theoretical calculations. Consequently, CoTAP-iCONs exhibit higher CO 2 RR activity than other cobalt-porphyrin-based structures: an 870% and 480% improvement of CO current densities compared to the monomer and neutral nanosheets, respectively. Additionally, the iCONs structure can accommodate the cationic moieties. In a flow cell, CoTAP-iCONs attain a very small onset overpotential of 40 mV and a stable total current density of 212 mA cm –2 with CO Faradaic efficiency of >95% at –0.6 V for 11 h. Further coupling the flow electrolyzer with commercial solar cells yields a solar-to-CO conversion efficiency of 13.89%. Finally, this work indicates that atom-thin, ionic nanosheets represent a promising structure for achieving both tailored activity and high stability.

36 MATERIALS SCIENCE↗