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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

INBOUND APPOINTEE PUBLICATION Importance of Substrate-Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach of positively-charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively-charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle-fiber and particle-substrate interactions. We find an inverse correlation between particle size and attachment density to the protein nanofibers, attributed to the balance between size-dependent electrostatic particle-fiber attraction and particle-substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically-observable particle assemblies.

Guye, Kathryn N.↗
DOE OSTI2020

The mutualism effector MiSSP7 of Laccaria bicolor alters the interactions between the poplar JAZ6 protein and its associated proteins

Despite the pivotal role of jasmonic acid in the outcome of plant-microorganism interactions, JA-signaling components in roots of perennial trees like western balsam poplar (Populus trichocarpa) are poorly characterized. Here we decipher the poplar-root JA-perception complex centered on PtJAZ6, a co-repressor of JA-signaling targeted by the effector protein MiSSP7 from the ectomycorrhizal basidiomycete Laccaria bicolor during symbiotic development. Through protein–protein interaction studies in yeast we determined the poplar root proteins interacting with PtJAZ6. Moreover, we assessed via yeast triple-hybrid how the mutualistic effector MiSSP7 reshapes the association between PtJAZ6 and its partner proteins. In the absence of the symbiotic effector, PtJAZ6 interacts with the transcription factors PtMYC2s and PtJAM1.1. In addition, PtJAZ6 interacts with it-self and with other Populus JAZ proteins. Finally, MiSSP7 strengthens the binding of PtJAZ6 to PtMYC2.1 and antagonizes PtJAZ6 homo-/heterodimerization. We conclude that a symbiotic effector secreted by a mutualistic fungus may promote the symbiotic interaction through altered dynamics of a JA-signaling-associated protein–protein interaction network, maintaining the repression of PtMYC2.1-regulated genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The impact of curation errors in the PDBBind Database on machine learning predictions of protein–protein binding affinity

The PDBBind database has been widely utilized for the computational prediction of protein–protein binding affinities. While the accuracy of the PDBBind-curated equilibrium dissociation constants (K D ) has been reported for the protein–ligand subset of the PDBBind database, the curation accuracy has not been reported for the protein–protein subset. Here, we present a detailed manual analysis for the subset of PDBBind records with PubMed Central Open Access primary publications and find that ~19% of these records had K D values that were not supported by their primary publications. The impact of these putative curation errors on the machine learning-based prediction of K D from experimental protein–protein 3D structures was evaluated and correcting the curation errors improved the Pearson correlation coefficient between measured and random forest-predicted log 10 (K D ) values by ~8 percentage points. This finding underscores the importance of dataset accuracy for computational modelling and highlights the need for more stringent curation processes when extracting information from the scientific literature.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

An Arabidopsis Ran-binding protein, AtRanBP1c, is a co-activator of Ran GTPase-activating protein and requires the C-terminus for its cytoplasmic localization

Ran-binding proteins (RanBPs) are a group of proteins that bind to Ran (Ras-related nuclear small GTP-binding protein), and thus either control the GTP/GDP-bound states of Ran or help couple the Ran GTPase cycle to a cellular process. AtRanBP1c is a Ran-binding protein from Arabidopsis thaliana (L.) Heynh. that was recently shown to be critically involved in the regulation of auxin-induced mitotic progression [S.-H. Kim et al. (2001) Plant Cell 13:2619-2630]. Here we report that AtRanBP1c inhibits the EDTA-induced release of GTP from Ran and serves as a co-activator of Ran-GTPase-activating protein (RanGAP) in vitro. Transient expression of AtRanBP1c fused to a beta-glucuronidase (GUS) reporter reveals that the protein localizes primarily to the cytosol. Neither the N- nor C-terminus of AtRanBP1c, which flank the Ran-binding domain (RanBD), is necessary for the binding of PsRan1-GTP to the protein, but both are needed for the cytosolic localization of GUS-fused AtRanBP1c. These findings, together with a previous report that AtRanBP1c is critically involved in root growth and development, imply that the promotion of GTP hydrolysis by the Ran/RanGAP/AtRanBP1c complex in the cytoplasm, and the resulting concentration gradient of Ran-GDP to Ran-GTP across the nuclear membrane could be important in the regulation of auxin-induced mitotic progression in root tips of A. thaliana.

NASA Discipline Plant Biology↗
NASA NTRS1992

Suppression of muscle protein turnover and amino acid degradation by dietary protein deficiency

To define the adaptations that conserve amino acids and muscle protein when dietary protein intake is inadequate, rats (60-70 g final wt) were fed a normal or protein-deficient (PD) diet (18 or 1% lactalbumin), and their muscles were studied in vitro. After 7 days on the PD diet, both protein degradation and synthesis fell 30-40% in skeletal muscles and atria. This fall in proteolysis did not result from reduced amino acid supply to the muscle and preceded any clear decrease in plasma amino acids. Oxidation of branched-chain amino acids, glutamine and alanine synthesis, and uptake of alpha-aminoisobutyrate also fell by 30-50% in muscles and adipose tissue of PD rats. After 1 day on the PD diet, muscle protein synthesis and amino acid uptake decreased by 25-40%, and after 3 days proteolysis and leucine oxidation fell 30-45%. Upon refeeding with the normal diet, protein synthesis also rose more rapidly (+30% by 1 day) than proteolysis, which increased significantly after 3 days (+60%). These different time courses suggest distinct endocrine signals for these responses. The high rate of protein synthesis and low rate of proteolysis during the first 3 days of refeeding a normal diet to PD rats contributes to the rapid weight gain ("catch-up growth") of such animals.

Non-NASA Center↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Soft matter approach for creating novel protein hydrogels using fractal whey protein assemblies as building blocks

In this study, mesoscopic sized fractal assembly (FA) particles were prepared using whey protein isolate; the coldset gelation properties of FA particles were investigated in-depth. Two types of FA particles (FA -62 and FA -90) with different mean sizes were synthesized through controlled thermal treatment of whey protein solutions at two concentrations (62 g/L and 90 g/L). Particle characteristics e.g. hydrodynamic radius, ζ-potential and surface hydrophobicity were dependent on pH and structure of FA. Transmission electron microscopy (TEM) observation confirmed the fractal morphology and small-angle X-ray scattering (SAXS) analysis suggested an internal fractal structure for the obtained FA particles. Eight cold-set FA protein gels (2 % w/v) were manufactured by controlling two gelling factors at two levels: pH (5.8 and 7.0) and Ca 2+ content (5 mM and 10 mM). Rheological characteristics in the large amplitude oscillatory shear regime revealed that pH 7.0 gels were softer and elastic while pH 5.8 gels were harder and brittle. Rheology Pipkin diagrams demonstrated that the strain softening/stiffening and the shear-thinning/thickening behaviors may be fine-tuned by manipulating the key gelation factors: e.g. FA structure, pH, and Ca 2+ . The entrainment speed-dependent friction coefficient curves showed that at an intermediate velocity regime (6-250 mm s -1 ), FA -90 particles induced hydrogels had superior lubrication effect compared to FA -62 gels. Further, this work demonstrated a food structure design approach regarding tuning texture and lubrication properties of protein gels without changing protein content and protein composition. The optimized protein hydrogels may be used as texturizer for "cleaner label" food formulas and/or as delivery system for carrying micronutrients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Defense against phytopathogens relies on efficient antimicrobial protein secretion mediated by the microtubule-binding protein TGNap1

Plant immunity depends on the secretion of antimicrobial proteins, which occurs through yet-largely unknown mechanisms. The trans-Golgi network (TGN), a hub for intracellular and extracellular trafficking pathways, and the cytoskeleton, which is required for antimicrobial protein secretion, are emerging as pathogen targets to dampen plant immunity. In this work, we demonstrate that tgnap1-2, a loss-of-function mutant of Arabidopsis TGNap1, a TGN-associated and microtubule (MT)-binding protein, is susceptible to Pseudomonas syringae (Pst DC3000). Pst DC3000 infected tgnap1-2 is capable of mobilizing defense pathways, accumulating salicylic acid (SA), and expressing antimicrobial proteins. The susceptibility of tgnap1-2 is due to a failure to efficiently transport antimicrobial proteins to the apoplast in a partially MT-dependent pathway but independent from SA and is additive to the pathogen-antagonizing MIN7, a TGN-associated ARF-GEF protein. Therefore, our data demonstrate that plant immunity relies on TGNap1 for secretion of antimicrobial proteins, and that TGNap1 is a key immunity element that functionally links secretion and cytoskeleton in SA-independent pathogen responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Predicting compatibility between ferredoxins and the Fe protein of nitrogenase using in silico protein modeling

Biological nitrogen fixation is the process by which certain bacteria and archaea use the enzyme nitrogenase to reduce atmospheric nitrogen into bioavailable ammonium. Engineering non‐nitrogen‐fixing organisms, like plants, to use nitrogenase could reduce dependency on synthetic fertilizer and mitigate the environmental impacts of industrial fertilizer production. However, nitrogenase activity requires delivery of reducing power by small electron carrying proteins known as ferredoxins and flavodoxins, and successfully engineering nitrogenase into new systems will require a mechanistic understanding of electron delivery by these proteins. Most organisms often have multiple ferredoxins, raising the question of which ferredoxin can support nitrogenase activity. The purpose of this study is to gain insight into how we can predict which ferredoxin is compatible with the Fe protein, the component of nitrogenase that interacts with ferredoxin or flavodoxin. Our in silico protein–protein docking simulations reveal that most ferredoxins and flavodoxins involved in nitrogen fixation have the shortest distance (≤10 Å) between their redox cofactor and the [4Fe‐4S] cluster of the Fe protein. We found shorter cofactor distance contributes to faster intermolecular electron tunneling rates. Bacterial ferredoxins that play a role in nitrogen fixation also exhibit more complementary interactions with the Fe protein than bacterial and plant ferredoxins not involved in this process. Heterologous expression of a set of ferredoxins from both nitrogen‐fixing and non‐nitrogen‐fixing bacteria in the diazotroph Rhodopseudomonas palustris supports our model‐derived prediction that shorter distances between the electron‐carrying cofactors favor nitrogenase compatibility. These findings offer a framework to predict and potentially enhance ferredoxin–nitrogenase compatibility, which will help to improve our ability to engineer nitrogen fixation into non‐nitrogen‐fixing organisms like plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Protein Surface Printer for Exploring Protein Domains

The surface of proteins is vital in determining protein functions. Herein, a program, Protein Surface Printer (PSP), is built that performs multiple functions in quantifying protein surface domains. Two proteins, PETase and cytochrome P450, are used to validate that the program supports atomistic simulations with different combinations of programs and force fields. A case study is conducted on the structural analysis of the spike proteins of SARS-CoV-2 and SARS-CoV and the human cell receptor ACE2. Although the surface domains of both spike proteins are highly similar, their receptor-binding domains (RBDs) and the O-linked glycan domains are structurally different. The O-linked glycan domain of SARS-CoV-2 is highly positively charged, which may promote binding to negatively charged human cells.

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

Structural basis for SARS-CoV-2 envelope protein recognition of human cell junction protein PALS1

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has created global health and economic emergencies. SARS-CoV-2 viruses promote their own spread and virulence by hijacking human proteins, which occurs through viral protein recognition of human targets. To understand the structural basis for SARS-CoV-2 viral-host protein recognition, here we use cryo-electron microscopy (cryo-EM) to determine a complex structure of the human cell junction protein PALS1 and SARS-CoV-2 viral envelope (E) protein. Our reported structure shows that the E protein C-terminal DLLV motif recognizes a pocket formed exclusively by hydrophobic residues from the PDZ and SH3 domains of PALS1. Our structural analysis provides an explanation for the observation that the viral E protein recruits PALS1 from lung epithelial cell junctions. In addition, our structure provides novel targets for peptide- and small-molecule inhibitors that could block the PALS1-E interactions to reduce E-mediated virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain–affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. Further, the integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2022

Effect of High-Protein Diets on Integrated Myofibrillar Protein Synthesis before Anterior Cruciate Ligament Reconstruction: A Randomized Controlled Pilot Study

Increasing dietary protein intake during periods of muscle disuse may mitigate the resulting decline in muscle protein synthesis (MPS). The purpose of this randomized pilot study was to determine the effect of increased protein intake during periods of disuse before anterior cruciate ligament (ACL) reconstruction on myofibrillar protein synthesis (MyoPS), and proteolytic and myogenic gene expression. Six healthy, young males (30 ± 9 y) were randomized to consume a high-quality, optimal protein diet (OP; 1.9 g·kg−1·d−1) or adequate protein diet (AP; 1.2 g·kg−1·d−1) for two weeks before ACL reconstruction. Muscle biopsies collected during surgery were used to measure integrated MyoPS during the intervention (via daily deuterium oxide ingestion) and gene expression at the time of surgery. MyoPS tended to be higher, with a large effect size in OP compared to AP (0.71 ± 0.1 and 0.54 ± 0.1%·d−1; p = 0.076; g = 1.56). Markers of proteolysis and myogenesis were not different between groups (p > 0.05); however, participants with greater MyoPS exhibited lower levels of MuRF1 gene expression compared to those with lower MyoPS (r = −0.82, p = 0.047). The data from this pilot study reveal a potential stimulatory effect of increased daily protein intake on MyoPS during injury-mediated disuse conditions that warrants further investigation.

Howard, Emily E. (ORCID:0000000179791236)↗