Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides
Abstract not provided
Gent, William E.↗
SEARCH · Engineering Papers
Results for “explainable”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Conformational dynamics linked to domain closure and substrate binding explain the ERAP1 allosteric regulation mechanism
The endoplasmic-reticulum aminopeptidase ERAP1 processes antigenic peptides for loading on MHC-I proteins and recognition by CD8 T cells as they survey the body for infection and malignancy. Crystal structures have revealed ERAP1 in either open or closed conformations, but whether these occur in solution and are involved in catalysis is not clear. Here, we assess ERAP1 conformational states in solution in the presence of substrates, allosteric activators, and inhibitors by small-angle X-ray scattering. We also characterize changes in protein conformation by X-ray crystallography, and we localize alternate C-terminal binding sites by chemical crosslinking. Structural and enzymatic data suggest that the structural reconfigurations of ERAP1 active site are physically linked to domain closure and are promoted by binding of long peptide substrates. These results clarify steps required for ERAP1 catalysis, demonstrate the importance of conformational dynamics within the catalytic cycle, and provide a mechanism for the observed allosteric regulation and Lys/Arg528 polymorphism disease association.
59 BASIC BIOLOGICAL SCIENCES↗
Sequence-specific dynamic DNA bending explains mitochondrial TFAM’s dual role in DNA packaging and transcription initiation
Abstract Mitochondrial transcription factor A (TFAM) employs DNA bending to package mitochondrial DNA (mtDNA) into nucleoids and recruit mitochondrial RNA polymerase (POLRMT) at specific promoter sites, light strand promoter (LSP) and heavy strand promoter (HSP). Herein, we characterize the conformational dynamics of TFAM on promoter and non-promoter sequences using single-molecule fluorescence resonance energy transfer (smFRET) and single-molecule protein-induced fluorescence enhancement (smPIFE) methods. The DNA-TFAM complexes dynamically transition between partially and fully bent DNA conformational states. The bending/unbending transition rates and bending stability are DNA sequence-dependent—LSP forms the most stable fully bent complex and the non-specific sequence the least, which correlates with the lifetimes and affinities of TFAM with these DNA sequences. By quantifying the dynamic nature of the DNA-TFAM complexes, our study provides insights into how TFAM acts as a multifunctional protein through the DNA bending states to achieve sequence specificity and fidelity in mitochondrial transcription while performing mtDNA packaging.
59 BASIC BIOLOGICAL SCIENCES↗
Aldehyde cool-flame chemistry explains a missing source of organic acids
Combustion emission is a significant source of organic acids, impacting atmospheric chemistry and climate. Their formation mechanisms, however, remain poorly understood, leading to underestimation in kinetic models. We investigate the cool-flame oxidation of key combustion intermediates—C 1 –C 4 aldehydes and benzaldehyde. Using in-situ synchrotron vacuum ultraviolet photoionization mass spectrometry, we observe the direct conversion of aldehydes to organic acids, a process enhanced by HO 2 radicals. Quantum chemistry calculations reveal that the reaction of RC(O)O 2 with HO 2 on the singlet potential energy surface contributes to organic acids. Incorporating this pathway into a kinetic model significantly improves organic acid prediction. Despite the high-temperature nature of engine combustion, significant spatial and temporal inhomogeneities (e.g., near-wall regions and crevice volumes) lead to localized cool-flame conditions, facilitating organic acid formation and emission. Elucidating the acid formation under cool-flame conditions provides a critical mechanism for accurately modelling anthropogenic organic acid emissions and developing mitigation strategies.
SVUV-PIMS↗
Can ferric-oxyl excited states explain elongated iron-oxygen bonds in heme peroxidase catalytic intermediates?
The use of X-ray structures to determine and interpret the ferryl iron-oxygen bond order in molecular oxygen-activating heme enzymes has, in the past, been controversial. This has mainly stemmed from the susceptibility of ferryl species to X-ray-induced electronic state changes. In this work we establishe using time-resolved serial femtosecond X-ray crystallography (tr-SFX) on a dye-decolourising peroxidase that the ferryl intermediate species (Compounds I and II) captured following in situ mixing of microcrystals with H 2 O 2 have single, rather than the double bond character expected. X-ray emission validated tr-SFX data with quantum refinement, time-dependent-DFT calculations and QM/MM geometry optimizations together support the concept that the single iron-oxygen bond character is not an indication of ferryl reduction or a protonated form (Fe IV -OH) but is instead attributed to the existence of accessible excited states possessing ferric-oxyl (Fe III –O •– ) character. Such states offer insight into the nature of ferryl heme.
Williams, Lewis J. [University of Essex, Colcheste↗
Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions
Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.
Atomistic models↗
Emergent actin flows explain distinct modes of gliding motility
During host infection, Toxoplasma gondii and related unicellular parasites move using gliding, which differs fundamentally from other known mechanisms of eukaryotic cell motility. Gliding is thought to be powered by a thin layer of flowing filamentous (F)-actin sandwiched between the plasma membrane and a myosin-covered inner membrane complex. How this surface actin layer drives the various gliding modes observed in experiments—helical, circular, twirling and patch, pendulum or rolling—is unclear. Here we suggest that F-actin flows arise through self-organization and develop a continuum model of emergent F-actin flow within the confines provided by Toxoplasma geometry. In the presence of F-actin turnover, our model predicts the emergence of a steady-state mode in which actin transport is largely directed rearward. Removing F-actin turnover leads to actin patches that recirculate up and down the cell, which we observe experimentally for drug-stabilized actin bundles in live Toxoplasma gondii parasites. These distinct self-organized actin states can account for observed gliding modes, illustrating how different forms of gliding motility can emerge as an intrinsic consequence of the self-organizing properties of F-actin flow in a confined geometry.
59 BASIC BIOLOGICAL SCIENCES↗
Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize
Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.
Genetics & Heredity↗
Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.
36 MATERIALS SCIENCE↗
Structural and functional analyses explain Pea KAI2 receptor diversity and reveal stereoselective catalysis during signal perception
KAI2 proteins are plant α/β hydrolase receptors which perceive smoke-derived butenolide signals and endogenous, yet unidentified KAI2-ligands (KLs). The number of functional KAI2 receptors varies among species and KAI2 gene duplication and sub-functionalization likely plays an adaptative role by altering specificity towards different KLs. Legumes represent one of the largest families of flowering plants and contain many agronomic crops. Prior to their diversification, KAI2 underwent duplication resulting in KAI2A and KAI2B. Here we demonstrate that Pisum sativum KAI2A and KAI2B are active receptors and enzymes with divergent ligand stereoselectivity. KAI2B has a higher affinity for and hydrolyses a broader range of substrates including strigolactone-like stereoisomers. We determine the crystal structures of PsKAI2B in apo and butenolide-bound states. The biochemical, structural, and mass spectra analyses of KAI2s reveal a transient intermediate on the catalytic serine and a stable adduct on the catalytic histidine, confirming its role as a bona fide enzyme. Our work uncovers the stereoselectivity of ligand perception and catalysis by diverged KAI2 receptors and proposes adaptive sensitivity to KAR/KL and strigolactones by KAI2B.
59 BASIC BIOLOGICAL SCIENCES↗
A conceptual model explaining spatial variation in soil nitrous oxide emissions in agricultural fields
Not Available
Zhang, Ziliang↗
When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins
Correlated wavefunction theory predicts and high-resolution crystal structure analysis confirms the important, stabilizing effect of simultaneous hydrogen bond donor and acceptor interactions in proteins.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Dilution and photooxidation driven processes explain the evolution of organic aerosol in wildfire plumes
Wildfires are a source of primary aerosols and precursors for secondary aerosols to the atmosphere. In this work, we discover that the evolution of these aerosols depends strongly on the coupled effects of dilution, photooxidation, and partitioning.
Akherati, Ali↗
Low quantum efficiency of μ-oxo iron bisporphyrin photocatalysts explained with femtosecond M-edge XANES
Bridged μ-oxo iron porphyrins serve as photocatalysts for oxidative organic transformations, but suffer from low photon-to-product efficiency. This low photochemical quantum yield is most commonly attributed to the short lifetime of a disproportionated TPPFe(II)/TPPFe(IV)=O state, but an alternate hypothesis suggests that the majority photoproduct is a catalytically inactive ligand-centered TPPFe(III)+/TPPFe(III)–O – ion pair. We use femtosecond optical and extreme ultraviolet (XUV) spectroscopy to investigate the early photodynamics of the μ-oxo iron bisporphyrin (TPPFe) 2 O and identify the primary loss mechanism. XUV spectroscopy probes 3p → 3d transitions, corresponding to M 2,3 -edge XANES spectra, and is a distinctive probe of the metal oxidation state. Excitation of the mixed π–π*/ligand-to-metal charge transfer (LMCT) band results in the formation of an iron(II)/iron(III) LMCT state in tens of femtoseconds. This state decays on a subpicosecond timescale to the ligand-centred iron(III) ion pair state, and no TPPFe(IV)=O species is observed within the sensitivity of the measurement. Finally, the lack of an iron(II)/iron(IV) XANES spectrum suggests that preferential formation of the inactive iron(III) ion pair state is a main cause of the low quantum yield of this and similar bisporphyrins.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Designing green chemicals by predicting vaporization properties using explainable graph attention networks
Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ACES-GNN: can graph neural network learn to explain activity cliffs?
Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Explaining an unusual electromigration behavior—A comprehensive experimental and theoretical analysis using finite element method
In metallic interconnects, it is generally assumed that electromigration (EM) failure location is independent of the applied electrical current and always occurs at the highest-current-density area. Our experiments show otherwise. We designed an Al interconnect that alters its failure location by only varying the applied current density. The failure occurs near the high for a current above 2 × 10 7 A/cm 2 , but at a location with 59% of the maximum for lower current densities. Thermoreflectance thermal imaging is employed to gather time-dependent high-resolution spatial temperature distributions of the Al interconnect during EM. More importantly, we propose a computationally inexpensive 2D finite element method that tracks EM evolution in time and matches well with the observations from different experimental conditions. A detailed analysis covering the major driving forces of EM is carried out to understand the complex physics behind EM. The atomic depletion rate contributed by each force is quantitatively studied. By examining the results from every tested experimental condition, the model reveals that the temperature gradient is the key reason causing atomic depletion near the failure location. Graphical illustrations and qualitative analysis are provided to intuitively show the key findings of our work.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Adjustments to the law of the wall above an Amazon forest explained by a spectral link
Modification to the law of the wall represented by a dimensionless correction function ϕ RSL (z/h) is derived using atmospheric turbulence measurements collected at two sites in the Amazon in near-neutral stratification, where z is the distance from the forest floor and h is the mean canopy height. The sites are the Amazon Tall Tower Observatory for z/h∈ [1,2.3] and the Green Ocean Amazon (GoAmazon) site for z/h∈ [1,1.4]. Here, a link between the vertical velocity spectrum E ww (k) (k is the longitudinal wavenumber) and ϕ RSL is then established using a co-spectral budget (CSB) model interpreted by the moving-equilibrium hypothesis. The key finding is that ϕ RSL is determined by the ratio of two turbulent viscosities and is given as ν t,BL /ν t,RSL, where ν t,RSL = (1/A)∫$^{∞}_{0}$ τ(k)E ww (k)dk, ν t,BL = k v (z−d)u * , τ(k) is a scale-dependent decorrelation time scale between velocity components, A = C R /(1−C I ) = 4.5 is predicted from the Rotta constant C R = 1.8, and the isotropization of production constant C I = 3/5 given by rapid distortion theory, k v is the von Kármán constant, u * is the friction velocity at the canopy top, and d is the zero-plane displacement. Because the transfer of energy across scales is conserved in E ww (k) and is determined by the turbulent kinetic energy dissipation rate (ε), the CSB model also predicts that ϕ RSL scales with L BL /L d , where L BL is the length scale of attached eddies to z = d, and L d = u$_{*}^{3}$/ε is a macro-scale dissipation length.
54 ENVIRONMENTAL SCIENCES↗