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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Interactions of Ku70/80 with Double-Strand DNA: Energetic, Dynamics, and Functional Implications

Space radiation is a proficient inducer of DNA damage leading to mutation, aberrant cell signaling, and cancer formation. Ku is among the first responding proteins in nucleus to recognize and bind the DNA double strand breaks (DSBs) whenever they are introduced. Once loaded Ku works as a scaffold to recruit other repair factors of non-homologous end joining and facilitates the following repair processes. The crystallographic study of the Ku70/80 heterodimer indicate the core structure of this protein shows virtually no conformational change after binding with DNA. To investigate the dynamical features as well as the energetic characteristics of Ku-DNA binding, we conduct multi-nanosecond molecular dynamics simulations of a modeled Ku70/80 structure and several complexes with two 24-bp DNA duplexes. Free energy calculations show significant energy differences between the complexes with Ku bound at DSBs and those with Ku associated at an internal site of a chromosome. The results also reveal detailed interactions between different nucleotides and the amino acids along the DNA-binding cradle of Ku, indicating subtle binding preference of Ku at specific DNA sequences. The covariance matrix analyses along the trajectories demonstrate the protein is stimulated to undergo correlated motions of different domains once bound to DNA ends. Additionally, principle component analyses identify these low frequency collective motions suitable for binding with and translocation along duplex DNA. It is proposed that the modification of dynamical properties of Ku upon binding with DSBs may provide a signal for the further recruitment of other repair factors such as DNA-PKcs, XLF, and XRCC4.

Hu, Shaowen↗
NASA NTRS2002

Normalized Cut Algorithm for Automated Assignment of Protein Domains

We present a novel computational method for automatic assignment of protein domains from structural data. At the core of our algorithm lies a recently proposed clustering technique that has been very successful for image-partitioning applications. This grap.,l-theory based clustering method uses the notion of a normalized cut to partition. an undirected graph into its strongly-connected components. Computer implementation of our method tested on the standard comparison set of proteins from the literature shows a high success rate (84%), better than most existing alternative In addition, several other features of our algorithm, such as reliance on few adjustable parameters, linear run-time with respect to the size of the protein and reduced complexity compared to other graph-theory based algorithms, would make it an attractive tool for structural biologists.

Samanta, M. P.↗
NASA NTRS2004

Structural Basis for the Catalytic Activity of Human Serine/Threonine Protein Phosphatase-5

Serinehhreonine protein phosphatase-5 (PP5) affects many signaling networks that regulate cell growth and cellular responses to stress. Here we report the crystal structure of the PP5 catalytic domain (PP5c) at a resolution of 1.6 A. From this structure we resolved the mechanism for PP5-mediated hydrolysis of phosphoprotein substrates, which requires the precise positioning of two metal ions within a con served Aspn-271-M(sub 1):M(sub 2)-W(sup 1)-His-427-His-304-Asp-274 catalytic motif. The structure of PPSc provides a structural basis for explaining the exceptional catalytic proficiency of protein phosphatases, which are among the most powerful known catalysts. Resolution of the entire C-terminus revealed a novel subdomain, and the structure of the PP5c should also aid development of type-specific inhibitors.

Swingle, M. R.↗
DOE OSTI2026

Structure-Aware Unsupervised, Transformational Machine Learning for Drug Discovery (DTRA Basic Research Final Report)

The major goal of this project is to develop machine learning (ML) methods to enable improved predictive power on real drug discovery for novel targets. More specifically, we planned to demonstrate the capability and effectiveness of ML tools utilizing unlabeled large-volume protein-ligand datasets. We investigated multiple pre-training approaches for 3D protein-ligand structure-based foundation models, without relying on experimental binding data. We also addressed scenarios in which crystal structures are unavailable or binding data are limited. We also planned to develop a complete pipeline to screen novel compounds as well as to demonstrate the capability and effectiveness of the developed methods by testing on a realistic drug discovery task such as SARS-CoV-2. While the major goals and milestones remain consistent with the original proposal, certain technical details have been adjusted, based on the experimental results and related outcomes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Molecular Design Principles for Photosystem I-Based Biohybrid Solar Fuel Catalysts

Direct solar-to-chemical conversion offers a compelling route to clean, dispatchable energy. Photosystem I (PSI), an evolutionarily optimized light-driven oxidoreductase, can be repurposed for solar-fuel production by coupling its photochemistry to catalytic interfaces. However, the molecular determinants that govern productive electron transfer to abiotic catalysts remain poorly understood. Here, we present molecular structures of active PSI-Pt nanoparticle (PtNP) biohybrids that reveal how protein architecture controls catalyst access, binding geometry, and photocatalytic efficiency. Removal of stromal subunits exposes the electron transfer chain and enables PtNP binding proximal to the F X cluster, demonstrating that steric occlusion limits access to native acceptor regions in PSI. In contrast, in trimeric PSI, PtNPs bind at multiple sites per monomer, but only a subset are positioned within electron transfer distance of terminal cofactors, resulting in a heterogeneous population of productive and nonproductive configurations. Structural analyses and molecular dynamics simulations define the interface topology, electrostatics, and cofactor-to-nanoparticle distances that govern catalyst binding and electron transfer. These results establish that catalytic inefficiency arises not only from intrinsic electron transfer constraints but also from the distribution of binding geometries imposed by the protein scaffold. Together, these findings provide a molecular framework linking protein structure to biohybrid function and define design principles for engineering PSI-based solar fuel systems and protein-nanomaterial interfaces for light-driven catalysis.

biohybrid↗
DOE OSTI2024

Solvent organization in the ultrahigh-resolution crystal structure of crambin at room temperature

Ultrahigh-resolution structures provide unprecedented details about protein dynamics, hydrogen bonding and solvent networks. The reported 0.70 Å, room-temperature crystal structure of crambin is the highest-resolution ambient-temperature structure of a protein achieved to date. Sufficient data were collected to enable unrestrained refinement of the protein and associated solvent networks using SHELXL . Dynamic solvent networks resulting from alternative side-chain conformations and shifts in water positions are revealed, demonstrating that polypeptide flexibility and formation of clathrate-type structures at hydrophobic surfaces are the key features endowing crambin crystals with extraordinary diffraction power.

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

Filling data analysis gaps in time-resolved crystallography by machine learning

There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Trujillo, Justin (ORCID:0000000285505360)↗
NASA NTRS1994

Evidence that the protocell was also a protoneuron

A blueprint for a protocell was presented in 1960 (Fox) as a consequence of the newly discovered self-ordering of amino acids and the self-organization of the resultant thermal proteins into cellular structures. The biofunctions of the laboratory protocells (proteinoid microspheres) have since been cataloged; they display roots of many phenomena of modern cells, e.g. synthesis of internucleotide and peptide bonds in aqueous media. These results are inconsistent with a popular assumption that DNA/RNA preceded protein in earliest molecular evolution. The necessity for synthetic research on molecules and cells to 'begin at the beginning' is being realized. Ivanov and Fortsch (1986) have described by analysis how the self-ordering mechanism of amino acids into informed thermal proteins was conserved in evolution from the earliest stage as modern (reverse) mechanisms assumed control. Tyagi and Ponnamperuma (1990) have negated assumptions corollary to DNA/RNA. Ponnamperuma has demonstrated the powerful effect of self-ordering of amino acids in polymerization of aminoacyl nucleotides and the irrelevance of mononucleotide residues. Excitable thermal proteins (Vaughan et al, 1987) are neurotrophic and antiaging when added to cultures of real neurons (Hefti et al, 1991) and are memory enhancers in mice (Fox and Flood, 1992). Proteinoid microspheres of dominant hydrophobic constitution form 'gap junctions', sprout axon-like outgrowths, and form dendritic networks spontaneously. In the latest studies, phenylalanine-rich equimolar proteinoid or the leucine analog (Ishima et al 1981), is found to produce electrical signals for several days when lecithin is included in the assembly with the thermal polymer.

Bi, YU↗
DOE OSTI2024

Exploration of Tertiary Structure in Sequence-Defined Polymers Using Molecular Dynamics Simulations

Peptoids are a class of sequence-defined biomimetic polymers with peptide-like backbones and side chains located on backbone nitrogens rather than alpha carbons. These materials demonstrate a strong ability for precise control of single-chain structure, multiunit self-assembly, and macromolecular assembly through careful tuning of sequence due to the diversity of available side chains, although the driving forces behind these assemblies are often not understood. Prior experimental work has shown that linked 15mer peptoids can mimic the protein helical hairpin structure by leveraging the chirality-inducing nature of bulky side chains and hydrophobicity, but there are still gaps in our understanding of the relationship between sequence, stability, and particular secondary or tertiary structure. Here, we present a molecular dynamics (MD) study on the folding behavior of these polymers into hairpins, discussing the differences in structure from sequences with various characteristics in water and acetonitrile, and then compare the handedness preference of common helical motifs between solvents.

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

Using Machine Learning to Improve Thermostability of MHETase

Protein engineering is a field which utilizes proteins as tools, which has many useful applications in medicine, industry, biofuels and more.1 One such protein is MHETase, which is a protein that plays an important function in the degradation of polyethylene terephthalate (PET) plastics, which are commonly used in water and soda bottles.2 However, these proteins are adapted to work in specific conditions, and may not satisfy the desired properties that a new application would desire, or could be improved. For instance, a more thermostable MHETase would be more effective in the plastic degradation conditions.3 To make these desired changes, the primary structure of the protein is mutated, but there are many possible mutations and positions to mutate to make with the 20 canonical amino acids. Therefore, to narrow down the possibilities and to make the process of finding a thermostable MHETase variant, we used sequence design tools that are grounded in machine learning to find mutations that would improve thermostability of MHETase.4 In particular, we used the tools Protein MPNN and Fireprot to design a more thermostable MHETase enzyme. We then compiled these mutations into a library and grew these proteins using bacteria colonies, and measured their effectiveness using a fluorescent protein marker. Thermostable proteins and their marker would fold correctly and fluorescence would be seen, but if neither folded correctly then there would be no marker detected. We grew these proteins in bacteria and then intend to use these methods to evaluate their thermostability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Improve Thermostability of MHETase

Protein engineering is a field which utilizes proteins as tools, which has many useful applications in medicine, industry, biofuels and more. One such protein is MHETase, which is a protein that plays an important function in the degradation of polyethylene terephthalate (PET) plastics, which are commonly used in water and soda bottles.2 However, these proteins are adapted to work in specific conditions, and may not satisfy the desired properties that a new application would desire, or could be improved. For instance, a more thermostable MHETase would be more effective in the plastic degradation conditions.3 To make these desired changes, the primary structure of the protein is mutated, but there are many possible mutations and positions to mutate to make with the 20 canonical amino acids. Therefore, to narrow down the possibilities and to make the process of finding a thermostable MHETase variant, we used sequence design tools that are grounded in machine learning to find mutations that would improve thermostability of MHETase.4 In particular, we used the tools Protein MPNN and FireProt to design a more thermostable MHETase enzyme. We then compiled these mutations into a library and grew these proteins using bacteria colonies, and measured their effectiveness using a fluorescent protein marker. Thermostable proteins and their marker would fold correctly and fluorescence would be seen, but if neither folded correctly then there would be no marker detected. We grew these proteins in bacteria and then intend to use these methods to evaluate their thermostability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural and biochemical analyses of the nuclear IκBζ protein in complex with the NF-κB p50 homodimer

As part of the efforts to understand nuclear IκB function in NF-κB-dependent gene expression, we report an X-ray crystal structure of the IκBζ ankyrin repeat domain in complex with the dimerization domain of the NF-κB p50 homodimer. IκBζ possesses an N-terminal α helix that conveys domain folding stability. Affinity and specificity of the complex depend on a small portion of p50 at the nuclear localization signal. The model suggests that only one p50 subunit supports binding with IκBζ, and biochemical experiments confirm that IκBζ associates with DNA-bound NF-κB p50:RelA heterodimers. Comparisons of IκBζ:p50 and p50:κB DNA complex crystallographic models indicate that structural rearrangement is necessary for ternary complex formation of IκBζ and p50 with DNA.

Cell Biology↗
NASA NTRS2002

Microencapsulated Bioactive Agents and Method of Making

Microcapsules prepared by encapsulating an aqueous solution of a protein, drug or other bioactive substance inside a semi-permeable membrane by are disclosed. The microcapsules are formed by interfacial coacervation under conditions where the shear forces are limited to 0- 100 dynes per sq cm at the interface. By placing the microcapsules in a high osmotic watering solution, the protein solution is gradually made saturated and then supersaturated, and the controlled nucleation and crystallization of the protein is achieved. The crystal-filled microcapsules prepared by this method can be conveniently harvested and stored while keeping the encapsulated crystals in essentially pristine condition due to the rugged, protective membrane. Because the membrane components themselves are x-ray transparent, large crystal-containing microcapsules can be individually selected. mounted in x-ray capillary tubes and subjected to high energy x-ray diffraction studies to determine the 3-D structure of the protein molecules. Certain embodiments of the microcapsules of the invention have composite polymeric outer membranes which are somewhat elastic, water insoluble, permeable only to water, salts, and low molecular weight molecules and are structurally stable in fluid shear forces typically encountered in the human vascular system.

Dennis R Morrison↗
DOE OSTI2025

PPI DataHub Project Data Package: S. elongatus PCC 7942 Limited Proteolysis and Thermal Proteome Profiling Structural Proteomics (JM-PB-DP3)

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques using limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and redox enrichment (Redox-MS) in evaluating alterations solvent accessibility and structural stability caused by light perturbation at the molecular level. Experimentally processed sample data for LiP and TPP proteomic datasets were derived from the same cell culture stock, prepared simultaneously in parallel, and acquired by mass spectrometry. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental samples processed for LiP-MS label-free quantification (LFQ) or TPP-MS tandem mass tag (TMT) 10-plex were acquired using a Q-Exactive HF-X mass spectrometer and processed/compiled using either MSGF+ (v2024.03.26) or ​​​​PlexedPiper for proteome evaluation. Additional software supporting downstream proteomic analysis include FragPipe (v.4.0), MSFragger (v.22.1), and an adapted Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and processed protein annotated abundance files.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Coupling of Charge Regulation and Geometry in Soft Ionizable Molecular Assemblies

The size, shape, and charge of structures, such as proteins and amphiphile assemblies, respond in an interconnected manner to solution ionic conditions. Here, we analyze assemblies of an amphiphile (C 16 K 2 ), with two ionizable amino acids [lysine (K)] coupled to a 16-carbon alkyl tail, via small-angle X-ray scattering (SAXS), nonlinear Poisson–Boltzmann theory (nl-PB), and hybrid Monte Carlo-molecular dynamics (MC-MD) simulations. SAXS revealed structural transitions from spherical micelles to cylindrical micelles to bilayers with increasing pH. By combining SAXS-determined structural information and nl-PB, we derived the molecular degree of ionization as a function of pH. The back-calculated titration curves matched the experimental data over an extended pH range, without adjustable parameters. Similarly, the SAXS data on the evolution of spherical micelle structure with ionic strength were combined with nl-PB and MC-MD to derive the bare and effective charges. MC-MD, which considered finite ion sizes, showed that bare and effective charges saturate quickly with increasing salt concentration. Furthermore, the calculated effective charges closely matched results from Zeta-potential measurements. The presented approach has advantages over customary methods for charge regulation, such as the Henderson–Hasselbalch (HH) or Hill models, where molecular ionization/deionization in assemblies is described by effective pKs that are distinct from the pK for isolated molecules. However, these models lack a physical explanation for these pK shifts. By contrast, our approach of combining structural details with an electrostatic model and simulations provides a more intuitive understanding of structure-charge coupling and a framework for understanding charge regulation in many synthetic and biological systems.

ionization↗
DOE OSTI2025

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (pdb-ihm.org), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology.

IHMCIF↗
DOE OSTI2026

Protocol for Engineered Compositional Asymmetry Within Nanodiscs

Membrane proteins remain the most challenging targets for structural characterization, yet their elucidation provides valuable insights into protein function, disease mechanisms, and drug specificity. Structural biology platforms have advanced rapidly in recent years, notably through the development and implementation of nanodiscs—discoidal lipid–protein complexes that encapsulate and solubilize membrane proteins within a controlled, native-like environment. While nanodiscs have become powerful tools for studying membrane proteins, faithfully reconstituting the compositional asymmetry intrinsic to nearly all biological membranes has not yet been achieved. Proper membrane leaflet lipid distribution is critical for accurate protein folding, stability, and insertion. Here, we share a protocol for reconstituting tailored compositional asymmetry within nanodiscs through membrane extraction from giant unilamellar vesicles (GUVs) treated with a leaflet-specific methyl-β-cyclodextrin (mβCD) lipid exchange. Nanodisc asymmetry is verified through a geometric approach: biotin-DPPE-preloaded mβCD engages in lipid exchange with the outer leaflet of POPC GUVs solubilized by the lipid-free membrane scaffold protein (MSP) Δ49ApoA-I to form nanodisc structures. Once isolated, nanodiscs are introduced to the biotin-binding bacterial protein streptavidin. High-speed atomic force microscopy imaging depicts nanodisc–dimer complexes, indicating that biotin-DPPE was successfully reconstituted into a single leaflet of the nanodiscs. This finding outlines the first step toward engineering tailored nanodisc asymmetry and mimicking the native environment of integral proteins—a potentially powerful tool for accurately reconstituting and structurally analyzing integral membrane proteins whose functions are modulated by lipid asymmetry.

Biological and medical sciences↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗