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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2022

Genetic variation in nitrogen‐use efficiency and its associated traits in dryland winter wheat ( Triticum aestivum L.) cultivars released from the 1940s to the 2010s in Shaanxi Province, China

Abstract BACKGROUND Improving the nitrogen‐use efficiency (NUE) of wheat can help mitigate the problems of poor soil fertility under dryland conditions. We conducted field experiments using three nitrogen (N) fertilization levels (0, 120, and 180 kg ha −1 ) applied to eight dryland wheat cultivars to assess NUE and its associated traits. RESULTS The grain yield significantly increased with the improvement in variety, mainly as a result of a substantial increase in 1000‐grain weight and harvest index. Modern wheat varieties have stabilized at an optimal plant height and exhibited improved performance in terms of NUE, partial N productivity, N harvest index, and grain protein content compared to older varieties. The NUE of wheat gradually increased with variety replacement. The net photosynthesis rate of the flag leaves in the filling stage improved with the year of cultivar release; Increasing soil–plant analysis development (SPAD) values of flag leaves in the flowering and filling stages were observed over time, with the flag leaves of modern varieties showing a high chlorophyll content in the filling stage. Additionally, the principal component analysis showed that the SPAD value, grain number per unit area, transpiration rate, leaf area, and grain protein content positively contributed to the clustering of the N180 and modern cultivars (from the 2000s to 2010s). CONCLUSION Overall, high levels of N application did not significantly improve the NUE of wheat. However, modern wheat varieties can optimize N distribution, increase flag leaf photosynthetic capacity, and improve photosynthesis ability, thus enhancing NUE to achieve high yields under a suitable level of N supply. © 2022 Society of Chemical Industry.

Lian, Huida↗
DOE OSTI2021

Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Novel Mutant Alleles Reveal a Role of the Extra-Large G Protein in Rice Grain Filling, Panicle Architecture, Plant Growth, and Disease Resistance

Plant growth and grain filling are the key agronomical traits for grain weight and yield of rice. The continuous improvement in rice yield is required for a future sustainable global economy and food security. The heterotrimeric G protein complex containing a canonical α subunit (RGA1) couples extracellular signals perceived by receptors to modulate cell function including plant development and grain weight. We hypothesized that, besides RGA1, three atypical, extra-large GTP-binding protein (XLG) subunits also regulate panicle architecture, plant growth, development, grain weight, and disease resistance. Here, we identified a role of XLGs in agronomic traits and stress tolerance by genetically ablating all three rice XLGs individually and in combination using the CRISPR/Cas9 genome editing in rice. For this study, eight (three single, two double, and three triple) null mutants were selected. Three XLG proteins combinatorically regulate seed filling, because loss confers a decrease in grain weight from 14% with loss of one XLG and loss of three to 32% decrease in grain weight. Null mutations in XLG2 and XLG4 increase grain size. The mutants showed significantly reduced panicle length and number per plant including lesser number of grains per panicle compared to the controls. Loss-of-function of all individual XLGs contributed to 9% more aerial biomass compared to wild type (WT). The double mutant showed improved salinity tolerance. Moreover, loss of the XLG gene family confers hypersensitivity to pathogens. Our findings suggest that the non-canonical XLGs play important roles in regulating rice plant growth, grain filling, panicle phenotype, stress tolerance, and disease resistance. Genetic manipulation of XLG s has the potential to improve agronomic properties in rice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Biochemical upgrading of high-protein biomass and grain products

The present invention relates to methods of upgrading biomass to provide useful chemical intermediates, fuels, amino acids, nutrients, etc. In particular examples, the biomass is a by-product of ethanol production and is mainly used as high-protein feed. Described herein are methods for upgrading such biomass, such as by implementing pre-treatment conditions and by employing fermentation conditions including modified organisms.

09 BIOMASS FUELS↗
DOE OSTI2022

Biochemical upgrading of high-protein biomass and grain products

The present invention relates to methods of upgrading biomass to provide useful chemical intermediates, fuels, amino acids, nutrients, etc. In particular examples, the biomass is a by-product of ethanol production and is mainly used as high-protein feed. Described herein are methods for upgrading such biomass, such as by implementing pre-treatment conditions and by employing fermentation conditions including modified organisms.

Davis, Ryan Wesley↗
DOE OSTI2021

Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction

The electrostatic potential (ESP) is a powerful property for understanding and predicting electrostatic charge distributions that drive interactions between molecules. In this study, we compare various charge partitioning schemes including fitted charges, density-based quantum mechanical (QM) partitioning schemes, charge equilibration methods, and our recently introduced coarse-grained electron model, C-GeM, to describe the ESP for protein systems. Furthermore, when benchmarked against high quality density functional theory calculations of the ESP for tripeptides and the crambin protein, we find that the C-GeM model is of comparable accuracy to ab initio charge partitioning methods, but with orders of magnitude improvement in computational efficiency since it does not require either the electron density or the electrostatic potential as input.

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

COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

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

Correction to “COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins”

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

Molecular interactions↗
DOE OSTI2022

Improving the Economic Viability of Biological Utilization of Coal Power Plant CO 2 by Improved Algae Productivity and Integration with Wastewater Treatment

The overall goal of this project was to improve the cost-effectiveness of systems using algae to capture and beneficially utilize CO 2 from the flue gas of coal-fired power plants. Algal biomass cultivation is an attractive alternative for carbon capture and utilization because it can uptake more CO 2 per land area than any other photosynthetic crop. Also, the resulting biomass can be used to make valuable large-market commodity products, such as biofuels and animal feed. However, the use of algal carbon capture has been limited by several factors including: (1) high costs for growing algae (>$\$1,300$/ton), (2) large land requirements for algae cultivation, and (3) environmental concerns about the large amount of water and nutrient inputs needed for growing algae. This project has taken direct aim at resolving these key limitations and demonstrated several viable approaches to improve the economic and environmental feasibility of algal carbon capture. In particular, this project demonstrated a significant increase in average annual algal productivity from baseline values of 8 g/m 2 -day to above 20 g/m 2 -day. This supports a much lower projected cost of algae production (<$\$500$/ton) and reduces land requirements by more than 50%. Additionally, this project confirmed the ability to grow multiple algae species with wastewater nutrients, which facilitates the potential for substantial new revenue streams for algae cultivation based on tipping fees for nutrient laden wastewater. This study highlighted that there is more potential value in wastewater nutrient treatment credits than in the primary commodity products made from algae. The use of wastewater inputs also mitigates the concerns associated with the large amount of water needed for growing algae. The algal biomass grown for this project using wastewater and simulated flue gas inputs were characterized for potential use as an animal feed ingredient. The algae had a protein content in the range of 35-40%, which is between some common commodity animal feed products— distiller’s grains (~25-30% protein) and soybean meal (45-50% protein). In-vitro digestion tests using cattle rumen fluids, showed that this algae biomass was best suited for use as “bypass protein”, which is digested in the animal intestine. Based on the protein content and amino acid profile of the algae grown in this study, it was estimated to have market value in the range of $\$190$-$\$280$/ton. There is potential for higher value feed products if the algae content of omega-3 fatty acids or antioxidants is accounted for. This project also did small scale demonstration of forward osmosis algae dewatering and nanofiltration of hydrothermal liquefaction aqueous products, which were shown to improve the cost and energy balance for producing algal products. When all the improvements demonstrated in this study were included in techno-economic analysis, it showed there was a viable pathway to profitable algal CO 2 utilization with a payback period in the range of six to ten years.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Towards resolving protein structures at the atomic scale using atom probe tomography

In the field of Structural Biology, Atom Probe Tomography (APT) is in the nascent stages of development wherein coarse-grained visuals of proteins have been captured. The characterization of organic samples or biomolecules through the technique is currently limited to the detection of a few dominant signatures. The problem of indecipherable characterization can inherently be traced back to multiple forms of technique-specific responses to organic samples and consequent triggers leading to organic-sample and sample-medium interactions. While it is possible for captured manifestations of protein reconstructions to seemingly appear intact from a basic visual purview, the parameter-protein associative responses throughout the structure as a direct consequence of the inherent workings of the technique (until harmonized with organic sample complexity and behavior) and field evaporation-based factors make non-aberrative atomic associations infeasible. The work focused on identifying and theorizing the (above stated and other) fundamental mechanisms that stronghold the study of intricate atomic to higher order associations in proteins through APT. Attempts at structure elucidation of the cryogenic sample under study, through indirect associations (and methods) based on other imaging techniques, further revealed the distinct and highly distortive nature at the atomic scale deterring structural tunability and thus characterization of APT based cryogenic samples under analysis. As a direct counter to the atomic scale characterization problem, by taking the experiment-specific uncertainties, and probable APT-centric organic sample-based variabilities into account, a basic result is extracted and presented. Through mass-spectrometric and computational analysis, specific individual amino acids (Sulfur-containing protein-bound amino acids) in proteins and aspects of protein structure (probable backbone fragments, partial sequence - partial backbone portions) have been identified and characterized. Under analysis considerations, a few of the simplest known and easily inferable segments that favor structural deteriorations in the reconstructions are stated. Additionally, to overcome technique-specific deterrents to the characterization of biomolecules in cryogenic sample medium, the development of a protein-labeling strategy tailored to APT is suggested.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Coarse-Grained Simulations for the Characterization and Optimization of Hybrid Protein–DNA Nanostructures

We present here the combination of experimental and computational modeling tools for the design and characterization of protein–DNA hybrid nanostructures. Our work incorporates several features in the design of these nanostructures: (1) modeling of the protein–DNA linker identity and length; (2) optimizing the design of protein–DNA cages to account for mechanical stresses; (3) probing the incorporation efficiency of protein–DNA conjugates into DNA nanostructures. The modeling tools were experimentally validated using structural characterization methods like cryo-TEM and AFM. Our method can be used for fitting low-resolution electron density maps when structural insights cannot be deciphered from experiments, as well as enable in-silico validation of nanostructured systems before their experimental realization. Finally, these tools will facilitate the design of complex hybrid protein–DNA nanostructures that seamlessly integrate the two different biomolecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Rice NIN-LIKE PROTEIN 4 plays a pivotal role in nitrogen use efficiency

Nitrogen (N) is one of the key essential macronutrients that affects rice growth and yield. Inorganic N fertilizers are excessively used to boost yield and generate serious collateral environmental pollution. Therefore, improving crop N use efficiency (NUE) is highly desirable and has been a major endeavour in crop improvement. However, only a few regulators have been identified that can be used to improve NUE in rice to date. Here we show that the rice NIN-like protein 4 (OsNLP4) significantly improves the rice NUE and yield. Field trials consistently showed that loss-of-OsNLP4 dramatically reduced yield and NUE compared with wild type under different N regimes. In contrast, the OsNLP4 overexpression lines remarkably increased yield by 30% and NUE by 47% under moderate N level compared with wild type. Transcriptomic analyses revealed that OsNLP4 orchestrates the expression of a majority of known N uptake, assimilation and signalling genes by directly binding to the nitrate-responsive cis-element in their promoters to regulate their expression. Moreover, overexpression of OsNLP4 can recover the phenotype of Arabidopsis nlp7 mutant and enhance its biomass. Our results demonstrate that OsNLP4 plays a pivotal role in rice NUE and sheds light on crop NUE improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A novel sub-pilot-scale direct-contact ultrasonic dehydration technology for sustainable production of distillers dried grains (DDG)

DDG is a major source of protein, calcium, phosphorus, and sulfur is arguably the most important byproduct of the bioethanol industry with increasing demand over the past few years. Reducing energy consumption in the DDG production process and energy recovery from DDG is vital for sustainable bioethanol productions. In this paper, a novel direct-contact multi-frequency, multimode, and modulated (MMM) ultrasonic dryer (US) was developed for the first time and has been applied in dehydration of wet distillers' grain (WDG). Ultrasonic drying (US) was combined with a convective airflow (HA) at different temperatures of 25 (room temperature), 50 and 70 °C to evaluate the impact of US, HA, and US + HA on drying kinetics, activation energy, chemical compositions, microstructure, and color of DDG. Semi-empirical kinetic models were developed and evaluating drying performances showed that the application of ultrasound significantly enhanced the drying rate and decreased the drying time (by 46%), especially at low drying temperatures. The activation energy for moisture removal in the presence of ultrasound was about 50% of that without ultrasound. The final dried distillers' grains product processed by ultrasonic drying had a brighter color, a higher available protein, a higher digestible protein (the lowest acid detergent insoluble crude protein), and a better surface profile with no compromise on minerals and fiber contents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗