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At least 289 records · Page 16

Structural evolution in Au- and Pd-based metallic glass forming liquids and the case for improved molecular dynamics force fields

The results of a combined experimental and computational investigation of the structural evolution of Au 81 Si 19 , Pd 82 Si 18 , and Pd 77 Cu 6 Si 17 metallic glass forming liquids are presented. Electrostatically levitated metallic liquids are prepared, and synchrotron x-ray scattering studies are combined with embedded atom method molecular dynamics simulations to probe the distribution of relevant structural units. Metal–metalloid based metallic glass forming systems are an extremely important class of materials with varied glass forming ability and mechanical processibility. High quality experimental x-ray scattering data are in poor agreement with the data from the molecular dynamics simulations, demonstrating the need for improved interatomic potentials. The first peak in the x-ray static structure factor in Pd 77 Cu 6 Si 17 displays evidence for a Curie–Weiss type behavior but also a peak in the effective Curie temperature. In conclusion, a proposed order parameter distinguishing glass forming ability, 1/(S(T,q 1 )−1, shows a peak in the effective Curie temperature near a crossover temperature established by the behavior of the viscosity, T A .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

59 BASIC BIOLOGICAL SCIENCES↗

Aerodynamics for the Mars Phoenix Entry Capsule

Pre-flight aerodynamics data for the Mars Phoenix entry capsule are presented. The aerodynamic coefficients were generated as a function of total angle-of-attack and either Knudsen number, velocity, or Mach number, depending on the flight regime. The database was constructed using continuum flowfield computations and data from the Mars Exploration Rover and Viking programs. Hypersonic and supersonic static coefficients were derived from Navier-Stokes solutions on a pre-flight design trajectory. High-altitude data (free-molecular and transitional regimes) and dynamic pitch damping characteristics were taken from Mars Exploration Rover analysis and testing. Transonic static coefficients from Viking wind tunnel tests were used for capsule aerodynamics under the parachute. Static instabilities were predicted at two points along the reference trajectory and were verified by reconstructed flight data. During the hypersonic instability, the capsule was predicted to trim at angles as high as 2.5 deg with an on-axis center-of-gravity. Trim angles were predicted for off-nominal pitching moment (4.2 deg peak) and a 5 mm off-axis center-ofgravity (4.8 deg peak). Finally, hypersonic static coefficient sensitivities to atmospheric density were predicted to be within uncertainty bounds.

Edquist, Karl T.↗

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detonation propulsion experiments and theory

Test data are presented for the use of a single detonation of explosives in long-cone, short-cone, straight, and firing-plug nozzles to provide propulsion in a simulated Jupiter atmosphere, as well as the ambient gases N, CO2 and He. The long-cone nozzle yielded a progressive increase with ambient pressure for the higher molecular weight gases CO2 and N, while the lower molecular weight He and simulated Jupiter atmosphere showed a specific pulse decrease with increasing ambient pressure. The short-plug nozzle yielded a small specific impulse reduction with increasing ambient pressure, and its results were found to be nearly independent of ambient gas molecular weight. All data gathered are analyzed by using first principles, approximate blast wave theory predictions, and two-dimensional numerical calculations. Rarefaction and oscillatory wave phenomena are found to significantly influence specific impulse.

Back, L. H.↗

Star formation in molecular clouds

A general overview is presented of the findings of molecular line and infrared astronomy regarding star formation. Beginning with the statement that stars, and therefore, planets, form from molecular clouds, it is argued that the growing body of data on conditions in molecular clouds should serve as input parameters for theoretical calculations of star and planet formation. The fact that the gravitational potential energy of molecular clouds far exceeds their thermal energy implies that the required rate of star formation is easily explained. Indeed the most important problem in this field is that the rate of star formation predicted from molecular cloud studies is too high. Until this problem is understood, additional modes of inducing star formation, while they may be occurring, only add to the existing difficulties.

Evans, N. J., II↗

Csa-19, a radiation-responsive human gene, identified by an unbiased two-gel cDNA library screening method in human cancer cells

A novel polymerase chain reaction (PCR)-based method was used to identify candidate genes whose expression is altered in cancer cells by ionizing radiation. Transcriptional induction of randomly selected genes in control versus irradiated human HL60 cells was compared. Among several complementary DNA (cDNA) clones recovered by this approach, one cDNA clone (CL68-5) was downregulated in X-irradiated HL60 cells but unaffected by 12-O-tetradecanoyl phorbol-13-acetate, forskolin, or cyclosporin-A. DNA sequencing of the CL68-5 cDNA revealed 100% nucleotide sequence homology to the reported human Csa-19 gene. Northern blot analysis of RNA from control and irradiated cells revealed the expression of a single 0.7-kilobase (kb) messenger RNA (mRNA) transcript. This 0.7-kb Csa-19 mRNA transcript was also expressed in a variety of human adult and corresponding fetal normal tissues. Moreover, when the effect of X- or fission neutron-irradiation on Csa-19 mRNA was compared in cultured human cells differing in p53 gene status (p53-/- versus p53+/+), downregulation of Csa-19 by X-rays or fission neutrons was similar in p53-wild type and p53-null cell lines. Our results provide the first known example of a radiation-responsive gene in human cancer cells whose expression is not associated with p53, adenylate cyclase or protein kinase C.

NASA Discipline Radiation Health↗

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Abstract Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme–substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Ferreiro, David↗

Value added data archiving

Researchers in the Molecular Sciences Research Center (MSRC) of Pacific Northwest Laboratory (PNL) currently generate massive amounts of scientific data. The amount of data that will need to be managed by the turn of the century is expected to increase significantly. Automated tools that support the management, maintenance, and sharing of this data are minimal. Researchers typically manage their own data by physically moving datasets to and from long term storage devices and recording a dataset's historical information in a laboratory notebook. Even though it is not the most efficient use of resources, researchers have tolerated the process. The solution to this problem will evolve over the next three years in three phases. PNL plans to add sophistication to existing multilevel file system (MLFS) software by integrating it with an object database management system (ODBMS). The first phase in the evolution is currently underway. A prototype system of limited scale is being used to gather information that will feed into the next two phases. This paper describes the prototype system, identifies the successes and problems/complications experienced to date, and outlines PNL's long term goals and objectives in providing a permanent solution.

Berard, Peter R.↗

Development of an Effective Finite-rate Oxidation Model for NuSil-coated Charred Carbon Preform Ablators

Recently, a detailed effective finite-rate surface chemistry model was developed for the oxidation of FiberForm [1] using the molecular beam experimental data of Poovathingal et al., [2]. FiberForm is the major building block of the thermal protection system (TPS) material Phenolic Impregnated Carbon Ablator (PICA), commonly used by NASA. The surface chemistry model consists of detailed surface reaction mechanisms such as adsorption, desorption, and several types of Langmuir-Hinshelwood (LH) reactions to characterize the oxygen-carbon interactions at the surface. This model provides excellent agreement with the experimental data for oxidation product compositions and corresponding translational energy distributions. Further, an effective oxidation model was constructed that captures the equivalent interaction of oxygen inside the microstructure (including multiple surface collisions) via a modified reactivity for a smooth wall boundary condition. This enables the use of this model directly into Computational Fluid Dynamics (CFD) codes and Material Response (MR) codes to accurately simulate the gas–surface interactions within FiberForm without using the detailed micro-structure. However, this effective model is valid only for the virgin FiberForm. As the TPS material undergoes ablation, the preform carbon burns and turns into char. In addition, PICA is also coated with a protective silicon coating called NuSil for the purpose of mitigating the spread of phenolic dust, and limit contamination during clean room operations. Fig. 1 shows a X-ray microtomography image of a charred NuSil-coated PICA. The NuSil layer introduces species containing silicon into the product mixture. Further, the reactivity of the carbon within the char layer is different from the virgin FiberForm as shown in Fig. 2. Recently a new set of molecular beam experiments were performed on this NuSil-coated TPS material [3] with the same type of oxygen beam used in the previous experiments. Using the latest experimental data, the previously developed effective model will be extended to account for the charred carbon as well as the NuSil coating. Finally, this new effective model with three phases – preform carbon, char, and NuSil; will be compared and validated against the experimental product compositions.

K Swaminathan Gopalan↗

Dust extinction and molecular gas in the dark cloud IC 5146

In this paper we describe a powerful method for mapping the distribution of dust through a molecular cloud using data obtained in large-scale, multiwavelength, infrared imaging surveys. This method combines direct measurements of near-infrared color excess and certain techniques of star counting to derive mean extinctions and map the dust column density distribution through a cloud at higher angular resolutions and greater optical depths than those achieved previously by optical star counting. We report the initial results of the application of this method to a dark cloud complex near the cluster IC 5146, where we have performed coordinated, near-infrared, JHK imaging and (13)CO, C(18)O, and CS millimeter-wave, molecular-line surveys of a large portion of the complex. More than 4000 stars were detected in our JHK survey of the cloud. Of these, all but about a dozen appear to be field stars not associated with the cloud. Star count maps at J band show a striking and detailed anticorrelation between the surface density of J-band sources and CO and CS molecular-line emission. We used the (H-K) colors and positions of nearly 1300 sources to directly measure and map the extinction and thus trace the dust column density through the cloud at an effective angular resolution of 1 min .5. We report an interesting correlation between the measured dispersion in our extinction determinations and the extinction. Modeling this relation indicates that effects of small-scale cloud structure dominate the uncertainties in our measurements. Moreover, we demonstrate that such observations can be used to place constraints on the nature of the spatial distribution of extinction on scales smaller than our resolution. In particular, we show that models in which the dust is distributed uniformly or in discrete high-extinction clumps on scales smaller than (1 min .5) are inconsistent with the observations. We have derived extinctions at the same positions and at the same angular resolution (1 min .7) as our molecular-line observations. This enabled a direct comparison of (13)CO, C(18)O, and CS integrated intensities and column densities with A(sub V) for more than 500 positions in the cloud, corresponding to a range in A(sub V) between 0 to 32 mag of extinction. We found the integrated intensities of (13)CO, C(18)O, and CS to be roughly linearly correlated with extinction over different ranges of extinction. However, for all three molecules we find the scatter in the observed relations to be larger than can be accounted for by instrumental error, suggesting that there are large intrinsic variations in the abundances or excitation of the molecules through the cloud. Mean abundances for all the molecules relative to hydrogen were directly derived from the data. The ratio of (13)CO to C(18)O abundances was found to be significantly higher than the terrestrial ratio in regions where extinction is less than 10 mag. In the same region, the dispersion in the abundance ratio is also found to be very large, suggesting that the abundances of one or both molecules are very unstable even at relatively large cloud optical depths. Beyond 10 mag of extinction the abundances of both species appear very stable with their ratio close to the terrestrial value.

Lada, Charles J.↗

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗

Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019–2023

Abstract Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019–2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).

60 APPLIED LIFE SCIENCES↗

Coherent fluctuations in a partially magnetized small magnetron discharge operating on molecular nitrogen

We report experimental data on rotating plasma disturbances in a quasi-steady direct-current magnetron discharge operating with molecular nitrogen at pressures of 27, 40, 67, and 133 Pa. Experiments with a segmented anode measuring the local current flow reveal up to 12 coherent structures (“spokes”) propagating in the +E×B and −E×B azimuthal directions. The current–voltage characteristics are studied and found to follow a negative resistance profile at the lower discharge currents. The existence of coherent structures and the number of spokes observed depend on the discharge current and operating pressure. Lower pressures are more favorable to the retrograde −E×B spoke rotation, which is observed in the 27, 40, and 67 Pa discharges. The results are interpreted with a gradient-driven drift wave model, and using the dispersion relation, we show that the propagation direction depends on the orientation of the local electric field within the plasma. Using an electron fluid model, we show how experimental conditions can lead to field reversals that generate −E×B spoke rotation in the lower pressure discharges.

Physics↗

Disk mass densities in edge-on spiral galaxies

Very Large Array (VLA) observations of the neutral hydrogen (HI) gas in two nearby edge-on spirals (NGC 4565 and NGC 891) successfully resolve the thickness of the gas layers in both disks over a wide range in radii. The combination of B, C, and D array data produces a 4 arcsec (approx. 200 pc) beam and 21 km s(exp -1) velocity resolution, combined with sensitivity to structures as large as 18 arcmin (approx. 54 kpc). These observations directly constrain the mid-plane disk mass densities, under the assumption of an equilibrium between the thermal pressure of the gas and the gravitational attraction of the disk. The results of a preliminary analysis are given regarding the z-velocity dispersion of the gas, the mass-to-light ratio of the disk in NGC 4565, and the roles of atomic and molecular gases. The data also allow a detailed study of the HI in these galaxies; in general their brightness temperature distributions seem similar to that in the Milky Way. Both galaxies show asymmetric HI extensions beyond the optical disk. In NGC 4565 the extension is a surprisingly abrupt warp, which may bend back to parallel the galactic plane; the velocity structure implies the warp is continuous around the disk.

Rupen, Michael P.↗

Liquid organic hydrogen carriers for long-duration energy storage

Liquid organic hydrogen carrier (LOHC) systems are an excellent alternative to pressurized gas and liquid hydrogen storage technologies due to their high volumetric storage capacities and straightforward adaptation to existing infrastructure. Here, we investigate various molecular and heterogenized Ru, Mn, and Fe catalysts for the reversible (de)hydrogenation of polyols. Mn catalysts, Mn-MACHO(Ph) (1) and Mn-MACHO(iPr)BH 4 (2-BH 4 ) were found to maintain catalytic activity for hydrogen production comparable to the Ru analogs, with greater than 98% conversion of 1,4 butanediol and quantitative hydrogen production. Furthermore, to assess the viability of utilizing heterogenized molecular catalysts for 1,4 butanediol (de)hydrogenation, molecular catalysts, Mn-MACHO(Ph), Ru-MACHO(Ph), and Ru-9, were polymerized to form Mn-MACHO-Poly, Ru-MACHO-Poly, and Ru-9-Poly respectively. These catalysts were then used to assess (de)hydrogenation of polyols, ethylene glycol and 1,4 butanediol. These studies reveal that Ru-MACHO-Poly is an efficient dehydrogenation catalyst with 99% conversion and a hydrogen percent yield of 96%. In addition, Ru-MACHO-Poly is a competent hydrogenation catalyst with approximately 98% conversion back to 1,4 butanediol based on quantitative NMR measurements. Overall, the data suggest these molecular and heterogenized catalysts have potential for practical use in polyalcohol-based LOHC systems.

08 HYDROGEN↗