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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Coarse-Grained Modeling Elucidates Differential Metabolism of Saccharomyces cerevisiae under Varied Nutrient Limitations

Microorganisms such as Saccharomyces cerevisiae have a native ability to adapt their metabolism to varying nutrient conditions. Understanding their responses to nutrient limitations is critical for decoding cellular physiology and designing strategies for metabolic engineering. While the influence of carbon availability on yeast metabolism has been extensively studied, the role of nitrogen availability remains relatively underexplored. In this study, we utilized a coarse-grained kinetic model to systematically analyze and compare the effects of carbon and nitrogen limitations on yeast metabolism. Our model successfully revealed the differential metabolic characteristics of S. cerevisiae under carbon- and nitrogen-limited chemostat conditions. It also highlighted the significance of protein activity regulation at varying carbon-to-nitrogen ratios, and elucidated distinct strategies employed to maintain ATP homeostasis. Furthermore, this study provides a computational tool for investigating yeast physiology under nutrient limitations and offers quantitative and mechanistic insights into yeast metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis project

The Annual Report presents the fiscal year (FY) 1988 research activities and accomplishments, for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division. The ECUT Biocatalysis Project is managed by the Jet Propulsion Laboratory, California Institute of Technology. The Biocatalysis Project is a mission-oriented, applied research and exploratory development activity directed toward resolution of the major generic technical barriers that impede the development of biologically catalyzed commercial chemical production. The approach toward achieving project objectives involves an integrated participation of universities, industrial companies and government research laboratories. The Project's technical activities were organized into three work elements: (1) The Molecular Modeling and Applied Genetics work element includes research on modeling of biological systems, developing rigorous methods for the prediction of three-dimensional (tertiary) protein structure from the amino acid sequence (primary structure) for designing new biocatalysis, defining kinetic models of biocatalyst reactivity, and developing genetically engineered solutions to the generic technical barriers that preclude widespread application of biocatalysis. (2) The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields and lower separation energetics. Results of work within this work element will be used to establish the technical feasibility of critical bioprocess monitoring and control subsystems. (3) The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the energy-economics of biocatalyzed chemical production processes, and initiation of technology transfer for advanced bioprocesses.

Baresi, Larry↗
NASA NTRS2001

Effects of Convective Transport of Solute and Impurities on Defect-Causing Kinetics Instabilities

For in-situ studies of the formation and evolution of step patterns during the growth of protein crystals, we have designed and assembled an experimental setup based on Michelson interferometry with the surface of the growing protein crystal as one of the reflective surfaces. The crystallization part of the device allows optical monitoring of a face of a crystal growing at temperature stable within 0.05 C in a developed solution flow of controlled direction and speed. The reference arm of the interferometer contains a liquid-crystal element that allows controlled shifts of the phase of the interferograms. We employ an image processing algorithm which combines five images with a pi/2 phase difference between each pair of images. The images are transferred to a computer by a camera capable of capturing 6-8 frames per second. The device allows data collection data regarding growth over a relatively large area (approximately .3 sq. mm) in-situ and in real time during growth. The estimated dept resolution of the phase shifting interferometry is about 100 A. The lateral resolution, depending on the zoom ratio, varies between 0.3 and 0.6 micrometers. We have now collected quantitative results on the onset, initial stages and development of instabilities in moving step trains on vicinal crystal surfaces at varying supersaturation, position on the facet, crystal size and temperature with the proteins ferritin, apoferritin and thaumatin. Comparisons with theory, especially with the AFM results on the molecular level processes, see below, allow tests of the rational for the effects of convective flows and, as a particular case, the lack thereof, on step bunching.

Vekilov, Peter G.↗
DOE OSTI2023

Computing the Relative Affinity of Chlorophylls a and b to Light-Harvesting Complex II

In plants and algae, the primary antenna protein bound to photosystem II is light-harvesting complex II (LHCII), a pigment–protein complex that binds eight chlorophyll (Chl) a molecules and six Chl b molecules. Chl a and Chl b differ only in that Chl a has a methyl group (–CH 3 ) on one of its pyrrole rings, while Chl b has a formyl group (–CHO) at that position. This blue-shifts the Chl b absorbance relative to Chl a . It is not known how the protein selectively binds the right Chl type at each site. Knowing the selection criteria would allow the design of light-harvesting complexes that bind different Chl types, modifying an organism to utilize the light of different wavelengths. The difference in the binding affinity of Chl a and Chl b in pea and spinach LHCII was calculated using multiconformation continuum electrostatics and free energy perturbation. Both methods have identified some Chl sites where the bound Chl type ( a or b ) has a significantly higher affinity, especially when the protein provides a hydrogen bond for the Chl b formyl group. However, the Chl a sites often have little calculated preference for one Chl type, so they are predicted to bind a mixture of Chl a and b . The electron density of the spinach LHCII was reanalyzed, which, however, confirmed that there is negligible Chl b in the Chl a -binding sites. Finally, it is suggested that the protein chooses the correct Chl type during folding, segregating the preferred Chl to the correct binding site.

chemical calculations↗
DOE OSTI2025

CFD Simulation of Aerobic Gas Fermentation to Enable Commercial Conversion of CO 2 into Aquaculture and Animal Feed: Cooperative Research and Development (Final Report)

NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.

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

TeraChem protocol buffers ( TCPB ): Accelerating QM and QM/MM simulations with a client–server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. Here, we describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google’s protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ~250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI - )—a model chromophore in green fluorescent proteins—on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Semiautomated, Reproducible Batch Processing of Soy

A computer-controlled apparatus processes batches of soybeans into one or more of a variety of food products, under conditions that can be chosen by the user and reproduced from batch to batch. Examples of products include soy milk, tofu, okara (an insoluble protein and fiber byproduct of soy milk), and whey. Most processing steps take place without intervention by the user. This apparatus was developed for use in research on processing of soy. It is also a prototype of other soy-processing apparatuses for research, industrial, and home use. Prior soy-processing equipment includes household devices that automatically produce soy milk but do not automatically produce tofu. The designs of prior soy-processing equipment require users to manually transfer intermediate solid soy products and to press them manually and, hence, under conditions that are not consistent from batch to batch. Prior designs do not afford choices of processing conditions: Users cannot use previously developed soy-processing equipment to investigate the effects of variations of techniques used to produce soy milk (e.g., cold grinding, hot grinding, and pre-cook blanching) and of such process parameters as cooking times and temperatures, grinding times, soaking times and temperatures, rinsing conditions, and sizes of particles generated by grinding. In contrast, the present apparatus is amenable to such investigations. The apparatus (see figure) includes a processing tank and a jacketed holding or coagulation tank. The processing tank can be capped by either of two different heads and can contain either of two different insertable mesh baskets. The first head includes a grinding blade and heating elements. The second head includes an automated press piston. One mesh basket, designated the okara basket, has oblong holes with a size equivalent to about 40 mesh [40 openings per inch (.16 openings per centimeter)]. The second mesh basket, designated the tofu basket, has holes of 70 mesh [70 openings per inch (.28 openings per centimeter)] and is used in conjunction with the press-piston head. Supporting equipment includes a soy-milk heat exchanger for maintaining selected coagulation temperatures, a filter system for separating okara from other particulate matter and from soy milk, two pumps, and various thermocouples, flowmeters, level indicators, pressure sensors, valves, tubes, and sample ports

Thoerne, Mary↗
DOE OSTI2020

Special Issue: To Commemorate the Legacy of Malcolm J. Andrews—Scientist, Educator, and Leader

This special issue celebrates the scientific legacy of Dr. Malcolm J. Andrews, former Editor of the ASME Journal of Fluids Engineering (2010–2018). Malcolm devoted his career to the development and applications of computational, experimental, and analytical tools to investigate and solve diverse, complex fluid dynamics, and heat transfer problems. Malcolm’s most notable research accomplishments include: (i) contributions to innovative experiments, simulations, and theory to understand buoyancydriven turbulent mixing due to Rayleigh–Taylor instability; (ii) development of the multiphase particle-in-cell method for understanding fragmentation in spray jets, coal-water slurry, and other dense particulate flows; (iii) mixing in nano- and biomaterials in applications ranging from protein glues to carbon nanotube composites; (iv) determining dispersion in proteins to improve therapeutic drug delivery via micro-encapsulation; (v) innovative methods for storing information using nanopinned dipole magnetic vortices in superconducting materials; and (vi) modeling and design modifications for shell-and-tube heat exchangers, as well as heat transfer in rotating systems such as turbine blades. The results of his research have been published in a long series of trend-setting journal and conference papers, book chapters, and patents.

42 ENGINEERING↗
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

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

Generating forces in confinement via polymerization

Understanding how to produce forces using biomolecular building blocks is essential for the development of adaptive synthetic cells and living materials. Here we ask whether a dynamic polymer system can generate deformation forces in soft shells by pure self-assembly, motivated by the fact that biological polymer networks like the cytoskeleton can exert forces, move objects, and deform membranes by simply growing, even in the absence of molecular motors. We address this question by investigating polymer force generation by varying the release rate, the structure, and the interactions of self-assembling monomers. First, we develop a toy computational model of polymerization in a soft elastic shell that reveals the emergence of spontaneous bundling which enhances shell deformation. We then extend our model to account more explicitly for monomer binding dynamics. We find that the rate at which monomers are released into the interior of the shell is a crucial parameter for achieving deformation through polymer growth. Lastly, we demonstrate that the introduction of multivalent particles that can join polymers can either improve or impede polymer performance, depending on the amount and on the structure of the multivalent particles. Our results provide guidance for the experimental realization of polymer systems that can perform work at the nanoscale, for example through rationally designed self-assembling proteins or nucleic acids.

Osmanović, Dino [University of California at Los A↗
NASA NTRS2000

Evolutionary Cell Computing: From Protocells to Self-Organized Computing

On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well-ordered, peptides is also critical to evolution of a non-genomic system. Based on these results, a new computational object called a "molnet" is defined. Like a neural network, it is formed of interconnected units that send "signals" to each other. Like molecules, neural networks have a specific function once their structure is defined. The difference between a molnet and traditional neural networks, is that input to molnets is not simply passed along and processed from input to output units, but rather it is utilized to form and break connections(bonds), and thus to form new structures. Molnets represent a powerful tool that can be used to understand the conditions under which chemical systems can form large molecules, such as proteins, and display ever more complex functions. This has direct applications, for example to the design of smart,synthetic fabrics. Additional information is contained in the original.

Colombano, Silvano↗
DOE OSTI2024

Evaluating the potential of disaggregated memory systems for HPC applications

Summary Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High‐performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system‐wide bisection bandwidth penalty. In addition, we also show that intra‐rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth.

Ding, Nan↗
DOE OSTI2024

Multi-omics Resources for Understanding Gene Regulation in Response to ER Stress in Plants

Proteotoxic stress of the endoplasmic reticulum (ER) is a potentially lethal condition that ensues when the biosynthetic capacity of the ER is overwhelmed. A sophisticated and largely conserved signaling, known as the unfolded protein response (UPR), is designed to monitor and alleviate ER stress. In plants, the emerging picture of gene regulation by the UPR now appears to be more complex than ever before, requiring multi-omics-enabled network-level approaches to be untangled. In the past decade, with an increasing access and decreasing costs of next-generation sequencing (NGS) and high-throughput protein–DNA interaction (PDI) screening technologies, multitudes of global molecular measurements, known as omics, have been generated and analyzed by the research community to investigate the complex gene regulation of plant UPR. In this chapter, we present a comprehensive catalog of omics resources at different molecular levels (transcriptomes, protein–DNA interactomes, and networks) along with the introduction of key concepts in experimental and computational tools in data generation and analyses. Finally, this chapter will serve as a starting point for both experimentalists and bioinformaticians to explore diverse omics datasets for their biological questions in the plant UPR, with likely applications also in other species for conserved mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Biochemistry & Molecular Biology↗
DOE OSTI2024

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants

Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

RCSB Protein Data Bank 1D tools and services

Abstract Motivation Interoperability between polymer sequences and structural data is essential for providing a complete picture of protein and gene features and helping to understand biomolecular function. Results Herein, we present two resources designed to improve interoperability between the RCSB Protein Data Bank, the NCBI and the UniProtKB data resources and visualize integrated data therefrom. The underlying tools provide a flexible means of mapping between the different coordinate spaces and an interactive tool allows convenient visualization of the 1-dimensional data over the web. Availabilityand implementation https://1d-coordinates.rcsb.org and https://rcsb.github.io/rcsb-saguaro. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Combining Physics and Machine Learning for the Next Generation of Molecular Simulation

Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21 st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens

Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first de novo designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins. Trimers that experimentally adopted their designed configurations were incorporated as components of tetrahedral, octahedral, and icosahedral nanoparticles, which were characterized by cryo-electron microscopy and assessed for their ability to present viral glycoproteins. Electron microscopy and antibody binding experiments demonstrated that the designed nanoparticles presented antigenically intact prefusion HIV-1 Env, influenza hemagglutinin, and RSV F trimers in the predicted geometries. This work demonstrates that antigen-displaying protein nanoparticles can be designed from scratch, and provides a systematic way to investigate the influence of antigen presentation geometry on the immune response to vaccination.

59 BASIC BIOLOGICAL SCIENCES↗