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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rapid and efficient ambient temperature X-ray crystal structure determination at Turkish Light Source

High-resolution biomacromolecular structure determination is essential to better understand protein function and dynamics. Serial crystallography is an emerging structural biology technique which has fundamental limitations due to either sample volume requirements or immediate access to the competitive X-ray beamtime. Obtaining a high volume of well-diffracting, sufficient-size crystals while mitigating radiation damage remains a critical bottleneck of serial crystallography. As an alternative, we introduce the plate-reader module adapted for using a 72-well Terasaki plate for biomacromolecule structure determination at a convenience of a home X-ray source. We also present the first ambient temperature lysozyme structure determined at the Turkish light source (Turkish DeLight). The complete dataset was collected in 18.5 min with resolution extending to 2.39 Å and 100% completeness. Combined with our previous cryogenic structure (PDB ID: 7Y6A), the ambient temperature structure provides invaluable information about the structural dynamics of the lysozyme. Turkish DeLight provides robust and rapid ambient temperature biomacromolecular structure determination with limited radiation damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Crystal structure of domain of unknown function 507 (DUF507) reveals a new protein fold

The crystal structure of the domain of unknown function family 507 protein from Aquifex aeolicus is reported (AaDUF507, UniProt O67633, 183 residues). The structure was determined in two space groups (C222 1 and P3 2 21) at 1.9 Å resolution. The phase problem was solved by molecular replacement using an AlphaFold model as the search model. AaDUF507 is a Y-shaped α-helical protein consisting of an anti-parallel 4-helix bundle base and two helical arms that extend 30-Å from the base. The two crystal structures differ by a 25° rigid body rotation of the C-terminal arm. The tertiary structure exhibits pseudo-twofold symmetry. The structural symmetry mirrors internal sequence similarity: residues 11–57 and 102–148 are 30% identical and 53% similar with an E-value of 0.002. In one of the structures, electron density for an unknown ligand, consistent with nicotinamide or similar molecule, may indicate a functional site. Docking calculations suggest potential ligand binding hot spots in the region between the helical arms. Structure-based query of the Protein Data Bank revealed no other protein with a similar tertiary structure, leading us to propose that AaDUF507 represents a new protein fold.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comprehensive characterization of the structure of Zr-based metallic glasses

Structure of metallic glasses fascinates as the generic amorphous structural template for ubiquitous systems. Its specification necessitates determination of the complete hierarchical structure, starting from short-range-order (SRO) → medium-range-order (MRO) → bulk structure and free volume (FV) distribution. This link has largely remained elusive since previous investigations adopted one-technique-at-a-time approach, focusing on limited aspects of any one domain. Reconstruction of structure from experimental data inversion is non-unique for many of these techniques. As a result, complete and precise structural understanding of glass has not emerged yet. In this work, we demonstrate the first experimental pathway for reconstruction of the integrated structure, for and glasses. Our strategy engages diverse (×7) multi-scale techniques [XAFS, 3D-APT, ABED/NBED, FEM, XRD, PAS, FHREM] on the same glass. This strategy complemented mutual limitations of techniques and corroborated common parameters to generate complete, self-consistent and precise parameters. Further, MRO domain size and inter-void separation were correlated to identify the presence of FV at MRO boundaries. This enabled the first experimental reconstruction of hierarchical subset: SRO → MRO → FV → bulk structure. The first ever image of intermediate region between MRO domains emerged from this link. We clarify that determination of all subsets is not our objective; the essence and novelty of this work lies in directing the pathway towards finite solution, in the most logical and unambiguous way.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database

Abstract Understanding lanthanide coordination chemistry can help develop new ligands for more efficient separation of lanthanides for critical materials needs. The Cambridge Structural Database (CSD) contains tens of thousands of single crystal structures of lanthanide complexes that can serve as a training ground for both fundamental chemical insights and future machine learning and generative artificial intelligence models. This work aims to understand the currently available structures of lanthanide complexes in CSD by analyzing the coordination shell, donor types, and ligand types, from the perspective of rare-earth element (REE) separations. We obtain four sets of lanthanide complexes from CSD: Subset 1, all Ln-containing complexes (49472 structures); Subset 2, mononuclear Ln complexes (27858 structures); Subset 3, mononuclear Ln complexes without cyclopentadienyl ligands (Cp) (26156 structures); Subset 4, Ln complexes with at least one 1,10-phenanthroline (phen) or its derivative as a coordinating ligand (2226 structures). The subsequent analysis of lanthanide complexes in these subsets examines the trends in coordination numbers and first shell distances as well as identifies and characterizes the ligands and donor groups. In addition, examples of Ln-complexes with commercially available complexants and phen-based ligands are interrogated in detail. This systematic investigation lays the groundwork for future data-driven ligand designs for REE separations based on the structural insights into the lanthanide coordination chemistry.

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

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2022

Experimentally informed structure optimization of amorphous TiO 2 films grown by atomic layer deposition

Amorphous titanium dioxide TiO 2 (a-TiO 2 ) has been widely studied, particularly as a protective coating layer on semiconductors to prevent corrosion and promote electron–hole conduction in photoelectrochemical reactions. The stability and longevity of a-TiO 2 is strongly affected by the thickness and structural heterogeneity, implying that understanding the structure properties of a-TiO 2 is crucial for improving the performance. This study characterized the structural and electronic properties of a-TiO 2 thin films (~17 nm) grown on Si by atomic layer deposition (ALD). Fluctuation spectra V(k) and angular correlation functions were determined with 4-dimensional scanning transmission electron microscopy (4D-STEM), which revealed the distinctive medium-range ordering in the a-TiO 2 film. A realistic atomic model of a-TiO 2 was established guided by the medium-range ordering and the previously reported short-range ordering of a-TiO 2 film, as well as the interatomic potential. The structure was optimized by the StructOpt code using a genetic algorithm that simultaneously minimizes energy and maximizes the match to experimental short- and medium-range ordering. The StructOpt a-TiO 2 model presents improved agreements with the medium-range ordering and the k-space location of the dominant 2-fold angular correlations compared with a traditional melt-quenched model. The electronic structure of the StructOpt a-TiO 2 model was studied by ab initio calculations and compared to the crystalline phases and experimental results. Finally, this work uncovered the medium-range ordering in a-TiO 2 thin films and provided a realistic a-TiO 2 structure model for further investigation of structure–property relationships and materials design. In addition, the improved multi-objective optimization package StructOpt was provided for structure determination of complex materials guided by experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Crystal structure prediction with host-guided inpainting generation and foundation potentials

Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. Here, we present the crystal host-guided generation (CHGGen) framework to address this challenge through conditional generation using an inpainting method, which optimizes a fraction of atomic positions within a predefined and symmetrized host structure to improve the success rate for symmetric structure generation. By integrating inpainting structure generation with a foundation potential for structure optimization, we demonstrate the method on the ZnS–P 2 S 5 and Li–Si chemical systems, where the inpainting method generates a higher fraction of symmetric structures than unconditional generation. The practical significance of CHGGen extends to enabling the structural modification of crystal structures, particularly for systems with partial occupancy or intercalation chemistry. The inpainting method also allows for seamless integration with other generative models, providing a versatile framework for accelerating materials discovery.

Zhong, Peichen [University of California, Berkeley↗
DOE OSTI2025

Impact of the d 0 transition metal on local structural transformations in disordered rock salt cathodes

Although it is widely accepted that the long-range (average) crystal structure plays a critical role in determining the electrochemical performance of battery materials, the relationship between local structural features and electrochemical performance is rarely studied. Disordered rock salt oxides (DRX), which have become serious contenders for next generation Li-ion electrode materials, provide an ideal platform for exploring correlations between local structure and electrochemical performance as they exhibit a simple face-centered cubic structure and combine long-range disorder and short-range order on the cation sublattice. This work examines the Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 series of DRX cathodes and investigates the links between local structure rearrangements and capacity activation. The end-member Li 1.1 Mn 0.7 Zr 0.2 O 2 compound exhibits a low capacity in the as-synthesized state, attributed to unfavorable short-range order that hinders Li-ion transport, yet its capacity increases seven-fold, from 20 to 140 mAh g −1 , after chemical delithiation followed by a 400 °C heat treatment. Capacity activation is associated with the appearance of local spinel-like structural features that depart from the short-range order originally present in the material, without significant change to the bulk composition and average crystal structure. Investigation of a series of Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 (x ≤ 0.2) DRX compounds reveals that the correlation length of the spinel-like ordering that emerges during the heat treatment strongly depends on the Zr : Ti ratio. Yet, dramatic capacity activation and electrochemical (pseudo-)plateaus reminiscent of Mn-based spinel cathodes are observed for all compounds irrespective of the size of the ordered domains. To explain this phenomenon, we propose that the DRX phase undergoes a complete transformation to a spinel-like domain structure, which improves bulk Li-ion transport regardless of domain size.

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

Topology-dependent stability of vortex-antivortex structures

The non-trivial topology of magnetic structures such as vortices and skyrmions is considered as a key concept to explain the stability of those structures. The stability, dictated by non-trivial topology, provides great potential for device applications. Although it is a very critical scientific and technological issue, it is elusive to experimentally study the topology-dependent stability owing to the difficulties in establishing stably formed magnetic structures with different topologies. Here, we establish a platform for vortex-antivortex structures with different topological charges within Ni 80 Fe 20 rectangular elements thick enough to stabilize a unique three-dimensional magnetic structure with non-uniform magnetization along the thickness of the elements. The detailed magnetization configurations of the three-dimensional vortex-antivortex structures and their annihilations during their field-driven motions are investigated by utilizing magnetic transmission soft x-ray microscopy and micromagnetic simulation. We demonstrate that the stability of vortex-antivortex structures significantly depends on their topologies and the topology-dependent stability is associated with their different annihilation mechanisms. Furthermore, we believe that this work provides in-depth insight into the stability of magnetic structures and its topology dependence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Repetitive proteins that undergo large conformational changes evade structural prediction algorithms

Protein structure prediction algorithms, such as AlphaFold, have accelerated protein design and advanced the understanding of the relationship between amino acid sequence and protein structure. However, these algorithms are limited in their ability to predict the structures of conformationally dynamic, intrinsically disordered, and stimuli-responsive proteins. To evaluate sequence-to-structure predictions of such challenging proteins, we explored a class of conformationally dynamic, repeats-in-toxin (RTX) proteins. RTX proteins adopt intrinsically disordered conformations in the absence of calcium and undergo reversible folding into β-roll structures upon binding to calcium. RTX proteins are characterized by tandem repeats of the sequence GGXGXDXUX, in which X can be any amino acid and U is an aliphatic amino acid. We designed RTX sequence variants with global substitutions of nonconserved amino acids, tandem repeats of consensus sequences GGAGXDTLY, and tandem repeats of scrambled sequences GGAGXDTYL. AlphaFold2 and AlphaFold3 predicted that all of these RTX variants adopt β-roll structures, characteristic of wild-type RTX bound to calcium. However, modeling the predicted structures with molecular dynamics simulations and characterizing the protein variants with circular dichroism spectroscopy, small-angle x-ray scattering, and x-ray crystallography revealed that variants adopt diverse, sequence-dependent structures in the absence and presence of calcium. To better design proteins for applications in biotechnology and sustainability, it is critical to build predictive tools that consider intrinsically disordered protein states and validate these tools with multi-mode, multi-scale experimental data.

Chang, Marina P. [Stanford Univ., CA (United State↗
DOE OSTI2024

Structure of saguaro cactus virus 3′ translational enhancer mimics 5′ cap for eIF4E binding

The genomes of several plant viruses contain RNA structures at their 3' ends called cap-independent translation enhancers (CITEs) that bind the host protein factors such as mRNA 5' cap-binding protein eIF4E for promoting cap-independent genome translation. However, the structural basis of such 5' cap-binding protein recognition by the uncapped RNA remains largely unknown. Here, we have determined the crystal structure of a 3' CITE, panicum mosaic virus-like translation enhancer (PTE) from the saguaro cactus virus (SCV), using a Fab crystallization chaperone. The PTE RNA folds into a three-way junction architecture with a pseudoknot between the purine-rich R domain and pyrimidine-rich Y domain, which organizes the overall structure to protrude out a specific guanine nucleotide, G18, from the R domain that comprises a major interaction site for the eIF4E binding. The superimposable crystal structures of the wild-type, G18A, G18C, and G18U mutants suggest that the PTE scaffold is preorganized with the flipped-out G18 ready to dock into the eIF4E 5' cap-binding pocket. The binding studies with wheat and human eIF4Es using gel electrophoresis and isothermal titration calorimetry, and molecular docking computation for the PTE–eIF4E complex demonstrated that the PTE structure essentially mimics the mRNA 5' cap for eIF4E binding. Such 5' cap mimicry by the uncapped and structured viral RNA highlights how viruses can exploit RNA structures to mimic the host protein-binding partners and bypass the canonical mechanisms for their genome translation, providing opportunities for a better understanding of virus-host interactions and non-canonical translation mechanisms found in many pathogenic RNA viruses.

3' cap-independent translation enhancers↗
DOE OSTI2022

Benchmarking structural evolution methods for training of machine learned interatomic potentials

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. Here, we benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler–Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning

Abstract The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural and functional insights into the interaction between the bacteriophage T4 DNA processing proteins gp32 and Dda

Abstract Bacteriophage T4 is a classic model system for studying the mechanisms of DNA processing. A key protein in T4 DNA processing is the gp32 single-stranded DNA-binding protein. gp32 has two key functions: it binds cooperatively to single-stranded DNA (ssDNA) to protect it from nucleases and remove regions of secondary structure, and it recruits proteins to initiate DNA processes including replication and repair. Dda is a T4 helicase recruited by gp32, and we purified and crystallized a gp32–Dda–ssDNA complex. The low-resolution structure revealed how the C-terminus of gp32 engages Dda. Analytical ultracentrifugation analyses were consistent with the crystal structure. An optimal Dda binding peptide from the gp32 C-terminus was identified using surface plasmon resonance. The crystal structure of the Dda–peptide complex was consistent with the corresponding interaction in the gp32–Dda–ssDNA structure. A Dda-dependent DNA unwinding assay supported the structural conclusions and confirmed that the bound gp32 sequesters the ssDNA generated by Dda. The structure of the gp32–Dda–ssDNA complex, together with the known structure of the gp32 body, reveals the entire ssDNA binding surface of gp32. gp32–Dda–ssDNA complexes in the crystal are connected by the N-terminal region of one gp32 binding to an adjacent gp32, and this provides key insights into this interaction.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Metastable piezoelectric group-IV monochalcogenide monolayers with a buckled honeycomb structure

Multiple two-dimensional materials are being naïvely termed stable on the grounds of displaying phonon dispersions with no negative frequencies and of not collapsing on molecular dynamics calculations at fixed volume. But, if these phases do not possess the smallest possible structural energy, how does one understand and establish their actual meta stability? To answer this question, twelve two-dimensional group-IV monochalcogenide monolayers (SiS, SiSe, SiTe, GeS, GeSe, GeTe, SnS, SnSe, SnTe, PbS, PbSe, and PbTe) with a buckled honeycomb atomistic structure—belonging to symmetry group P3m1—displaying an out-of-plane intrinsic electric polarization are shown to be metastable by three independent methods. First, we uncover a coordination-preserving structural transformation from the low-buckled honeycomb structure onto the lower-energy Pnm2 1 (or Pmmn for PbS, PbSe, and PbTe) phase to estimate energy barriers E B that must be overcome during such structural transformation. Using the curvature of the local minima and E B as inputs to Kramers escape formula, large escape times are found, implying the structural metastability of the buckled honeycomb phase (with the exception of PbS and PbSe, these phases display escape times ranging from 700 years to multiple times the age of the universe and can be considered “stable” for practical purposes in that relative sense). The second demonstration is provided by phonon dispersion relations that include the effect of long-range Coulomb forces and display no negative vibrational modes. The third and final demonstration of structural metastability is furnished by room-temperature ab initio molecular dynamics for selected compounds. Here, the magnitude of the electronic band gap evolves with chemical composition. Different from other binary two-dimensional compounds such as transition metal dichalcogenide monolayers and hexagonal boron nitride monolayers which only develop an in-plane piezoelectric response, the twelve group-IV monochalcogenide monolayers with a buckled honeycomb structure also display out-of-plane piezoelectric properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Assessment of thermally driven local structural phase changes in 1⁢𝑇′−MoTe 2

The role of layer disorder is important in establishing the topological phases of MoTe 2 . A rich tapestry of atomic ordering influences the structural phase transitions (SPTs), but there is little understanding of the mechanistic details of the phase transition. An atomistic level study was conducted to investigate the local structure of the 1⁢𝑇′ and 𝑇 𝑑 phases of MoTe 2 by using the pair distribution function (PDF) technique. While the average structure exhibits an SPT and coexistence of phases as a function of temperature, the local structure showed the suppression of SPT. The sample retained its monoclinic structure at all temperatures in short-range order. A sharp PDF peak observed at short distances indicated a strong atom-atom correlation between the Mo and Te atoms within the Mo octahedra. In addition, a large-box modeling of the PDF data indicated a preferential motion of Te atoms towards 𝑐 axis at all temperatures. Structural defects, such as stacking faults, likely result in the coexistence of phases in the average structure and suppress the local SPT of MoTe 2 . These results are stepping stones to understand the long-debated origins of structural, vibrational, and electronic properties of MoTe 2 and similar transition metal dichalcogenides.

2-dimensional systems↗
DOE OSTI2023

Decoding structure-spectrum relationships with physically organized latent spaces

Here, a semisupervised machine learning method for the discovery of structure-spectrum relationships is developed and then demonstrated using the specific example of interpreting x-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of >50 000 simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature but also reveal unintuitive ones that are visually indiscernible in large datasets. The results suggest that the RankAAE methodology has great potential to assist researchers in interpreting complex scientific data, testing physical hypotheses, and revealing patterns that extend scientific insight.

36 MATERIALS SCIENCE↗