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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structure of metallochaperone in complex with the cobalamin-binding domain of its target mutase provides insight into cofactor delivery

G-protein metallochaperone MeaB in bacteria [methylmalonic aciduria type A (MMAA) in humans] is responsible for facilitating the delivery of adenosylcobalamin (AdoCbl) to methylmalonyl-CoA mutase (MCM), the only AdoCbl-dependent enzyme in humans. Genetic defects in the switch III region of MMAA lead to the genetic disorder methylmalonic aciduria in which the body is unable to process certain lipids. Here, we present a crystal structure of Methylobacterium extorquens MeaB bound to a nonhydrolyzable guanosine triphosphate (GTP) analog guanosine-5'-[(β,γ)-methyleno]triphosphate (GMPPCP) with the Cbl-binding domain of its target mutase enzyme (MeMCM cbl ). This structure provides an explanation for the stimulation of the GTP hydrolyase activity of MeaB afforded by target protein binding. We find that upon MCM cbl association, one protomer of the MeaB dimer rotates ~180°, such that the inactive state of MeaB is converted to an active state in which the nucleotide substrate is now surrounded by catalytic residues. Importantly, it is the switch III region that undergoes the largest change, rearranging to make direct contacts with the terminal phosphate of GMPPCP. These structural data additionally provide insights into the molecular basis by which this metallochaperone contributes to AdoCbl delivery without directly binding the cofactor. Our data suggest a model in which GTP-bound MeaB stabilizes a conformation of MCM that is open for AdoCbl insertion, and GTP hydrolysis, as signaled by switch III residues, allows MCM to close and trap its cofactor. Substitutions of switch III residues destabilize the active state of MeaB through loss of protein:nucleotide and protein:protein interactions at the dimer interface, thus uncoupling GTP hydrolysis from AdoCbl delivery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Cholesterol-dependent enzyme activity of human TSPO1

The amino acid sequence of the tryptophan-rich sensory proteins (TSPO) is substantially conserved throughout all kingdoms of life. Human mitochondrial TSPO1 (HsTSPO1) binds to porphyrins and steroids, although its interactions with these molecules remains unknown.HsTSPO1 is associated with numerous physiological and pathological disorders, but the underlying molecular mechanisms are unknown. Here, we disclose the finding of human mitochondrial TSPO as a cholesterol-dependent protoporphyrin IX oxygenase. The results of our biochemical characterization are consistent with structural data and evolutionary analysis. The dependence ofHsTSPO1 activity on cholesterol may be the result of the coevolution of this membrane protein with the membrane system. Our study provides a molecular foundation for comprehending the various roles played by mitochondrial TSPO in normal physiological and pathological situations.

Science & Technology - Other Topics↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

On efficient Hessian computation using the edge pushing algorithm in Julia

Evaluating the Hessian matrix of second-order derivatives at a sequence of points can be costly when applying second-order methods for nonlinear optimization. In this work, we discuss our experiences implementing the recently proposed Edge Pushing (EP) method in Julia as an experimental replacement for the current colouring-based methods used by JuMP, an open-source algebraic modelling language. We propose an alternative data structure for sparse Hessians to avoid the use of hash tables and analyse the space and time complexity of EP method. In our benchmarks, we find that EP is very competitive in terms of both preprocessing time and Hessian evaluation time. As a result, we identify cases where EP closes the performance gap between JuMP's previous implementation and the implementation in AMPL, a commercial software package with similar functionality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Traffic safety analysis and model updating for freeways using Bayesian method

Freeway crash prediction models are the basic of traffic safety research, yet crash occurrence and the influencing factors change over time. In order to make sure the implemented safety models fit the current traffic environment, this study conducts a comparative analysis of 2017 and 2020 datasets collected from freeways in Suzhou, China. Herein, considering the spatial correlation among analysis units and the hierarchical data structure, a Bayesian conditional autoregressive negative binomial (CAR-NB) model and a Bayesian hierarchical CAR-NB (HCAR-NB) model were used to explore the safety influencing factors, and a traditional NB model was developed for further comparison. To update the HCAR-NB model from 2017 to 2020, Bayesian inference with informative priors was used to improve its goodness of fit and efficiency. Preliminary results showed that 1) the HCAR-NB model outperformed the NB model and CAR-NB model in prediction accuracy, and 2) the number of crashes was significantly correlated with average speed, speed variance, road segment length, number of lanes, and presence of ramps. The potential for safety improvement (PSI) method was applied to the modeling results to identify hotspots for the two years. The results confirmed that the hotspots spatiotemporally shifted among the freeways. The proposed crash prediction model and updating method are expected to assist implementation of informed countermeasures for freeway safety improvement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Static multiresolution grids with inline hierarchy information for cosmic ray propagation

For numerical simulations of cosmic-ray propagation fast access to static magnetic field data is required. We present a data structure for multiresolution vector grids which is optimized for fast access, low overhead and shared memory use. The hierarchy information is encoded into the grid itself, reducing the memory overhead. Benchmarks show that in certain scenarios the differences in deflections introduced by sampling the magnetic field model can be significantly reduced when using the multiresolution approach.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Hiding neutrino mass in modified gravity cosmologies

Cosmological observables show a dependence with the neutrino mass, which is partially degenerate with parameters of extended models of gravity. We study and explore this degeneracy in Horndeski generalized scalar-tensor theories of gravity. Using forecasted cosmic microwave background and galaxy power spectrum datasets, we find that a single parameter in the linear regime of the effective theory dominates the correlation with the total neutrino mass. For any given mass, a particular value of this parameter approximately cancels the power suppression due to the neutrino mass at a given redshift. The extent of the cancellation of this degeneracy depends on the cosmological large-scale structure data used at different redshifts. We constrain the parameters and functions of the effective gravity theory and determine the influence of gravity on the determination of the neutrino mass from present and future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Consolidating WLCG topology and configuration in the Computing Resource Information Catalogue

Here, the Worldwide LHC Computing Grid infrastructure links about 200 participating computing centres affiliated with several partner projects. It is built by integrating heterogeneous computer and storage resources in diverse data centres all over the world and provides CPU and storage capacity to the LHC experiments to perform data processing and physics analysis. In order to be used by the experiments, these distributed resources should be well described, which implies easy service discovery and detailed description of service configuration. Currently this information is scattered over multiple generic information sources like GOCDB, OIM, BDII and experiment-specific information systems. Such a model does not allow to validate topology and configuration information easily. Moreover, information in various sources is not always consistent. Finally, the evolution of computing technologies introduces new challenges. Experiments are more and more relying on opportunistic resources, which by their nature are more dynamic and should also be well described in the WLCG information system. This contribution describes the new WLCG configuration service CRIC (Computing Resource Information Catalogue) which collects information from various information providers, performs validation and provides a consistent set of UIs and APIs to the LHC VOs for service discovery and usage configuration. The main requirements for CRIC are simplicity, agility and robustness. CRIC should be able to be quickly adapted to new types of computing resources, new information sources, and allow for new data structures to be implemented easily following the evolution of the computing models and operations of the experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Structures of Neisseria gonorrhoeae MtrR-operator complexes reveal molecular mechanisms of DNA recognition and antibiotic resistance-conferring clinical mutations

Abstract Mutations within the mtrR gene are commonly found amongst multidrug resistant clinical isolates of Neisseria gonorrhoeae, which has been labelled a superbug by the Centers for Disease Control and Prevention. These mutations appear to contribute to antibiotic resistance by interfering with the ability of MtrR to bind to and repress expression of its target genes, which include the mtrCDE multidrug efflux transporter genes and the rpoH oxidative stress response sigma factor gene. However, the DNA-recognition mechanism of MtrR and the consensus sequence within these operators to which MtrR binds has remained unknown. In this work, we report the crystal structures of MtrR bound to the mtrCDE and rpoH operators, which reveal a conserved, but degenerate, DNA consensus binding site 5′-MCRTRCRN4YGYAYGK-3′. We complement our structural data with a comprehensive mutational analysis of key MtrR-DNA contacts to reveal their importance for MtrR-DNA binding both in vitro and in vivo. Furthermore, we model and generate common clinical mutations of MtrR to provide plausible biochemical explanations for the contribution of these mutations to multidrug resistance in N. gonorrhoeae. Collectively, our findings unveil key biological mechanisms underlying the global stress responses of N. gonorrhoeae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Chemical Biology And Endomembrane Trafficking In Plants

Endomembrane trafficking is essential for coordinated growth and development in plants and response to environment. In particular, key plasma membrane proteins such as PIN auxin transporters and the BRI1 brassinosteroid receptor translocate dynamically between the endosomes and plasma membrane. We utilized a previously published large‐scale chemical library screening approach in which we identified small molecules that inhibited pollen germination or tube growth. This tip growth requires exocytosis, endocytosis and other endomembrane trafficking processes We then identified groups of small molecules that affect trafficking of PIN auxin transporters and other plasma membrane proteins in Arabidopsis roots. One of these compounds (ES2) targets the essential EXO70A1 component of the exocyst complex in Arabidopsis which is necessary for normal development. In Arabidopsis, there are 23 EXO70 genes (in mammals and yeast they are low copy number) where, among other roles, they appear to be involved in autophagy, cell division and pathogen responses. ES2 targets the exocyst complex in plants as well as mammals including humans where defective recycling/exocytosis have been correlated with disease. To our knowledge, ES2 is the first bioactive compound known to have this specificity. To examine exocytosis and its regulation in more detail, we have examined exo70 mutants, generated EXO70A1 structural data and are examining additional small molecules that may affect recycling/exocytosis. Support or Funding Information Acknowledgement: This work was supported by the US Department of Energy DE‐FG02‐02ER15295 (GRH, NVR).

Hicks, Glenn R.↗
DOE OSTI2025

Three-dimensional lattice modulations in the charge density wave system Lu 2 Ir 3 Si 5

Using total and resonant x-ray scattering coupled to large-scale computer modeling, we study the lattice modulations in the complex charge density wave (CDW) material Lu 2 ⁢Ir 3 ⁢Si 5 . Here, we find that it is a unique quantum system where periodic lattice modulations related to emergent CDW order occur in three orthogonal atomic planes of the crystal lattice, leading to the emergence of an unusual three-dimensional (3D) pattern of short and long Ir-Ir and Lu-Lu bonds. The 3D character of observed lattice modulations explains the largely isotropic character of the changes in the electronic properties occurring when the CDW order sets in, demonstrating the strong electron-lattice coupling in Lu 2 ⁢Ir 3 ⁢Si 5 . The result is supported by DFT calculations based on the experimental structure data. Altogether, our work provides strong evidence for the presence of a relationship between the dimensionality of emergent lattice distortions and that of concurrent changes in the electronic properties of CDW materials. The relationship may need to be accounted for when these materials are explored for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Heterogeneous Activation, Local Structure, and Softness in Supercooled Colloidal Liquids

We experimentally characterize heterogeneous nonexponential relaxation in bidisperse supercooled colloidal liquids utilizing a recent concept called “softness”. Particle trajectory and structure data enable classification of particles into subgroups with different local environments and propensities to hop. We determine residence times t R between particle hops and show that t R derived from particles in the same softness subgroup are exponentially distributed. Using the mean residence time t¯ R for each softness subgroup, and a Kramers’ reaction rate model, we estimate the activation energy barriers E b for particle hops, and show that both t¯ R and E b are monotonic functions of softness. Lastly, we derive information about the combinations of large and small particle neighbors that determine particle softness, and we explicitly show that multiple exponential relaxation channels in the supercooled liquid give rise to its nonexponential behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A scripting interface for GSAS-II

The GSAS-II suite is a complete crystallographic analysis package for all types of X-ray and neutron diffraction data, suited for fitting models ranging from simple materials to proteins, and designed around an integrated graphical user interface. This article describes a new module within GSAS-II , GSASIIscript­able , which provides access to the GSAS-II data structures and an extensible mechanism to run a significant fraction of GSAS-II functionality, particularly for powder diffraction applications, from within Python scripts or directly from commands in a shell/batch script. In conclusion, this allows parallelization in a high-performance computing environment; near ideal speedup is seen with up to 240 simultaneous processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

pyXPCSviewer : an open-source interactive tool for X-ray photon correlation spectroscopy visualization and analysis

pyXPCSviewer , a Python-based graphical user interface that is deployed at beamline 8-ID-I of the Advanced Photon Source for interactive visualization of XPCS results, is introduced. pyXPCSviewer parses rich X-ray photon correlation spectroscopy (XPCS) results into independent PyQt widgets that are both interactive and easy to maintain. pyXPCSviewer is open-source and is open to customization by the XPCS community for ingestion of diversified data structures and inclusion of novel XPCS techniques, both of which are growing demands particularly with the dawn of near-diffraction-limited synchrotron sources and their dedicated XPCS beamlines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Phosphoramidon inhibits the integral membrane protein zinc metalloprotease ZMPSTE24

The integral membrane protein zinc metalloprotease ZMPSTE24 possesses a completely novel structure, comprising seven long kinked transmembrane helices that encircle a voluminous 14 000 Å 3 cavity within the membrane. Functionally conserved soluble zinc metalloprotease residues are contained within this cavity. As part of an effort to understand the structural and functional relationships between ZMPSTE24 and soluble zinc metalloproteases, the inhibition of ZMPSTE24 by phosphoramidon [ N -(α-rhamnopyranosyl-oxyhydroxyphosphinyl)-Leu-Trp], a transition-state analog and competitive inhibitor of multiple soluble zinc metalloproteases, especially gluzincins, has been characterized functionally and structurally. The functional results, the determination of preliminary IC 50 values by the use of an intramolecular quenched-fluorescence fluorogenic peptide assay, indicate that phosphoramidon inhibits ZMPSTE24 in a manner consistent with competitive inhibition. The structural results, a 3.85 Å resolution X-ray crystal structure of a ZMPSTE24–phosphoramidon complex, indicate that the overall binding mode observed between phosphoramidon and soluble gluzincins is conserved. Based on the structural data, a significantly lower potency than that observed for soluble gluzincins such as thermolysin and neprilysin is predicted. These results strongly suggest a close relationship between soluble gluzincins and the integral membrane protein zinc metalloprotease ZMPSTE24.

Goblirsch, Brandon R.↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗