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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Uncertainty-Proof Hosting Capacity with Surrogate Affine Policy

Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.

hosting capacity↗
DOE OSTI2022

Label-free affinity screening, design and synthesis of inhibitors targeting the Mycobacterium tuberculosis L-alanine dehydrogenase

The ability of Mycobacterium tuberculosis (Mtb) to persist in its host may enable an evolutionary advantage for drug resistant variants to emerge. A potential strategy to prevent persistence and gain drug efficacy is to directly target the activity of enzymes that are crucial for persistence. We present a method for expedited discovery and structure-based design of lead compounds by targeting the hypoxia-associated enzyme L-alanine dehydrogenase (AlaDH). Biochemical and structural analyses of AlaDH confirmed binding of nucleoside derivatives and showed a site adjacent to the nucleoside binding pocket that can confer specificity to putative inhibitors. Using a combination of dye-ligand affinity chromatography, enzyme kinetics and protein crystallographic studies, we show the development and validation of drug prototypes. Crystal structures of AlaDH-inhibitor complexes with variations at the N6 position of the adenyl-moiety of the inhibitor provide insight into the molecular basis for the specificity of these compounds. We describe a drug-designing pipeline that aims to block Mtb to proliferate upon re-oxygenation by specifically blocking NAD accessibility to AlaDH. The collective approach to drug discovery was further evaluated through in silico analyses providing additional insight into an efficient drug development strategy that can be further assessed with the incorporation of in vivo studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Affine Transformations to Correlate Experimental and Simulated EDS Spectra for Multi-Element Systems

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands

Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Chemistry↗
DOE OSTI2021

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARSCoV. The key to understanding SARS-CoV-2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV-2's spike protein stronger than SARS-CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X-ray structures. To go beyond insights gained from X-ray structures and investigate the role of thermal fluctuations in structure, we employ all-atom molecular dynamics simulations. Microseconds-long simulations reveal that while CoV and CoV-2 spike-ACE2 interfaces have similar conformational binding modes, CoV-2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non-interfacial residues. Furthermore, these results recommend that ongoing efforts to design spike-ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Calcitonin Receptor N-Glycosylation Enhances Peptide Hormone Affinity by Controlling Receptor Dynamics

The class B G protein-coupled receptor (GPCR) calcitonin receptor (CTR) is a drug target for osteoporosis and diabetes. N-glycosylation of asparagine 130 in its extracellular domain (ECD) enhances calcitonin hormone affinity with the proximal GlcNAc residue mediating this effect through an unknown mechanism. Here, we present two crystal structures of salmon calcitonin-bound, GlcNAc-bearing CTR ECD at 1.78 and 2.85 Å resolutions and analyze the mechanism of the glycan effect. The N130 GlcNAc does not contact the hormone. Surprisingly, the structures are nearly identical to a structure of hormone-bound, N-glycan-free ECD, which suggested that the GlcNAc might affect CTR dynamics not observed in the static crystallographic snapshots. Hydrogen-deuterium exchange mass spectrometry and molecular dynamics simulations revealed that glycosylation stabilized a β-sheet adjacent to the N130 GlcNAc and the N-terminal α-helix near the peptide-binding site while increasing flexibility of the peptide-binding site turret loop. These changes due to N-glycosylation increased the ligand on-rate and decreased its off-rate. The glycan effect extended to RAMP-CTR amylin receptor complexes and was also conserved in the related CGRP receptor. These results reveal that N-glycosylation can modulate GPCR function by altering receptor dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗
DOE OSTI2020

Heterogenized Pyridine-Substituted Cobalt(II) Phthalocyanine Yields Reduction of CO2 by Tuning the Electron Affinity of the Co Center

Conversion of CO 2 to reduced products is a promising route to alleviate irreversible climate change. In this paper we report the synthesis of a Co-based phthalocyanine with pyridine moieties (CoPc-Pyr), which is supported on a carbon electrode and shows Faradaic efficiency ~90% for CO at 490 mV of overpotential (-0.6 V vs reversible hydrogen electrode (RHE)). In addition, its catalytic activity at -0.7 V versus RHE surpasses other Co-based molecular and metal-organic framework catalysts for CO 2 reduction at this bias. Density functional theory calculations show that pyridine moieties enhance CO 2 adsorption and electron affinity of the Co center by an inductive effect, thus lowering the overpotential necessary for CO 2 conversion. Our study shows that CoPc-Pyr reduces CO 2 at lower overpotential and with higher activity than noble metal electrodes, such as silver.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Redox-Based Electrochemical Affinity Sensor for Detection of Aqueous Pertechnetate Anion

Rapid, selective, in-situ detection of TcO4- in multicomponent matrices consisting of interfering anions such as the ubiquitous NO3- and Cl- or the isostructural and isoelectronic CrO42- is challenging. Present sensors mostly lack the selectivity to exclude these interferences, or the sensitivity to meet the detection limits that are lower than the drinking water standards across the globe. This work presents an affinity based electrochemical sensor for TcO4- detection that relies on selective reductive precipitation of aqueous TcO4- induced by a capture probe immobilized on an electrode platform. This results in a direct decrease of the electron transfer current, the magnitude of the decrease being proportional to the amount of TcO4- added. Using this approach, we were able to achieve a detection limit of 1x10-10 M, which is lower than the drinking water standard of 5.2x10-10 M set by United States Environmental Protection Agency. Our proposed approach also allowed us to detect TcO4- from a multicomponent groundwater sample obtained from a well at the Hanford site in Washington (well 299-W19-36) that also contained NO3- , Cl- and CrO42-, without discernably affecting the detection limits.

Chatterjee, Sayandev↗
DOE OSTI2023

Optimal Binding Affinity for Sieving Separation of Propylene from Propane in an Oxyfluoride Anion-Based Metal–Organic Framework

Highly efficient adsorptive separation of propylene from propane offers an ideal alternative method to replace the energy-intensive cryogenic distillation technology. Molecular sieving-type separation via high-performance adsorbents is targeted for superior selectivity, but the limit in adsorption capacity remains a great challenge. Here, we report an oxyfluoride-based ultramicroporous metal–organic framework UTSA-400, [Ni(WO 2 F 4 )(pyz) 2 ] (pyz = pyrazine), featuring one-dimensional pore channels that can accommodate the propylene molecules with optimal binding affinity while specifically excluding the propane molecules. The exposed oxide/fluoride pairs in UTSA-400 serve as strong functional sites for strengthened propylene–host interactions, accounting for a significantly enhanced propylene uptake, while the propane molecules are excluded due to the regulated host framework dynamics. The strong propylene binding enables near-saturation of propylene in the pore confinement at ambient conditions, leading to full utilization of pore space and superior packing density. Combined in situ infrared spectroscopy measurements and dispersion-corrected density functional theory calculations clearly unveil the nature of boosted host–guest binding. Direct production of polymer-grade (>99.5%) propylene with remarkable dynamic productivity is demonstrated by column breakthrough experiments. Furthermore, this work presents an example of pore engineering with atomic precision to break the trade-off in adsorptive separation through guest binding optimization.

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

Tailoring Hierarchical Structure and Rare Earth Affinity of Compositionally Identical Polymers via Sequence Control

Macromolecule sequence, structure, and function are inherently intertwined. While well-established relationships exist in proteins, they are more challenging to define for synthetic polymer nanoparticles due to their molecular weight, sequence, and conformational dispersities. Furthermore, to explore the impact of sequence on nanoparticle structure, we synthesized a set of 16 compositionally identical, sequence-controlled polymers with distinct monomer patterning of dimethyl acrylamide and a bioinspired, structure-driving di(phenylalanine) acrylamide (FF). Sequence control was achieved through multiblock polymerizations, yielding unique ensembles of polymer sequences which were simulated by kinetic Monte Carlo simulations. Systematic analysis of the global (tertiary- and quaternary-like) structure in this amphiphilic copolymer series revealed the effect of multiple sequence descriptors: the number of domains, the hydropathy of terminal domains, and the patchiness (density) of FF within a domain, each of which impacted both chain collapse and the distribution of single- and multichain assemblies. Furthermore, both the conformational freedom of chain segments and local-scale, β-sheet-like interactions were sensitive to the patchiness of FF. To connect sequence, structure, and target function, we evaluated an additional series of nine sequence-controlled copolymers as sequestrants for rare earth elements (REEs) by incorporating a functional acrylic acid monomer into select polymer scaffolds. We identified key sequence variables that influence the binding affinity, capacity, and selectivity of the polymers for REEs. Collectively, these results highlight the potential of and boundaries of sequence control via multiblock polymerizations to drive primary sequence ensembles hierarchical structures, and ultimately the functionality of compositionally identical polymeric materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Structural basis for potent neutralization of SARS-CoV-2 and role of antibody affinity maturation

SARS-CoV-2 is a betacoronavirus virus responsible for the COVID-19 pandemic. Here, we determine the X-ray crystal structure of a potent neutralizing monoclonal antibody, CV30, isolated from a patient infected with SARS-CoV-2, in complex with the receptor binding domain. The structure reveals that CV30 binds to an epitope that overlaps with the human ACE2 receptor binding motif providing a structural basis for its neutralization. CV30 also induces shedding of the S1 subunit, indicating an additional mechanism of neutralization. A germline reversion of CV30 results in a substantial reduction in both binding affinity and neutralization potential indicating the minimal somatic mutation is needed for potently neutralizing antibodies against SARS-CoV-2.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Affinity-matured homotypic interactions induce spectrum of PfCSP structures that influence protection from malaria infection

The generation of high-quality antibody responses to Plasmodium falciparum (Pf) circumsporozoite protein (PfCSP), the primary surface antigen of Pf sporozoites, is paramount to the development of an effective malaria vaccine. Here we present an in-depth structural and functional analysis of a panel of potent antibodies encoded by the immunoglobulin heavy chain variable (IGHV) gene IGHV3-33, which is among the most prevalent and potent antibody families induced in the anti-PfCSP immune response and targets the Asn-Ala-Asn-Pro (NANP) repeat region. Cryo-electron microscopy (cryo-EM) reveals a remarkable spectrum of helical antibody-PfCSP structures stabilized by homotypic interactions between tightly packed fragments antigen binding (Fabs), many of which correlate with somatic hypermutation. We demonstrate a key role of these mutated homotypic contacts for high avidity binding to PfCSP and in protection from Pf malaria infection. Together, these data emphasize the importance of anti-homotypic affinity maturation in the frequent selection of IGHV3–33 antibodies and highlight key features underlying the potent protection of this antibody family.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Homo- and hetero-dimeric subunit interactions set affinity and efficacy in metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are dimeric class C G-protein–coupled receptors that operate in glia and neurons. Glutamate affinity and efficacy vary greatly between the eight mGluRs. The molecular basis of this diversity is not understood. We used single-molecule fluorescence energy transfer to monitor the structural rearrangements of activation in the mGluR ligand binding domain (LBD). In saturating glutamate, group II homodimers fully occupy the activated LBD conformation (full efficacy) but homodimers of group III mGluRs do not. Strikingly, the reduced efficacy of Group III homodimers does not arise from differences in the glutamate binding pocket but, instead, from interactions within the extracellular dimerization interface that impede active state occupancy. By contrast, the functionally boosted mGluR II/III heterodimers lack these interface ‘brakes’ to activation and heterodimer asymmetry in the flexibility of a disulfide loop connecting LBDs greatly favors occupancy of the activated conformation. Our results suggest that dimerization interface interactions generate substantial functional diversity by differentially stabilizing the activated conformation. This diversity may optimize mGluR responsiveness for the distinct spatio-temporal profiles of synaptic versus extrasynaptic glutamate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The dynamic adsorption affinity of ligands is a surrogate for the passivation of surface defects

Abstract Surface defects in semiconducting materials, though they have been widely studied, remain a prominent source of loss in optoelectronic devices; here we sought a new angle of approach, looking into the dynamic roles played by surface defects under atmospheric stressors and their chemical passivants in the lifetime of optoelectronic materials. We find that surface defects possess properties distinct from those of bulk defects. ab initio molecular dynamics simulations reveal a previously overlooked reversible degradation mechanism mediated by hydrogen vacancies. We find that dynamic surface adsorption affinity (DAA) relative to surface treatment ligands is a surrogate for passivation efficacy, a more strongly-correlated feature than is the static binding strength emphasized in prior reports. This guides us to design targeted passivator ligands with high molecular polarity: for example, 4-aminobutylphosphonic acid exhibits strong DAA and provides defect passivation applicable to a range of perovskite compositions, including suppressed hydrogen vacancy formation, enhanced photovoltaic performances and operational stability in perovskite solar cells.

Science & Technology - Other Topics↗
DOE OSTI2022

The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase

Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Proton affinities of pertechnetate (TcO 4 - ) and perrhenate (ReO 4 - )

The anions pertechnetate, TcO 4 - , and perrhenate, ReO 4 - , exhibit very similar chemical and physical properties. Revealing and understanding disparities between them enhances fundamental understanding of both. Electrospray ionization generated the gas-phase proton bound dimer (TcO 4 - )(H + )(ReO 4 - ). Collision induced dissociation of the dimer yielded predominantly HTcO 4 - and ReO 4 - , which according to Cooks’ kinetic method indicates that the proton affinity (PA) of TcO 4 - is greater than that of Re O 4 - . Density functional theory computations agree with the experimental observation, providing PA[TcO 4 - ] = 300.1 kcal mol -1 and PA[ReO 4 - ] = 297.2 kcal mol -1 . Attempts to rationalize these relative PAs based on elementary molecular parameters such as atomic charges indicate that the entirety of bond formation and concomitant bond disruption needs to be considered to understand the energies associated with such protonation processes. Although in both the gas and solution phases, TcO 4 - is a stronger base than ReO 4 - , it is noted that the significance of even such qualitative accordance is tempered by the very different natures of the underlying phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗