Atomic Structure Models Research Articles

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Functionalized Amorphous Carbon Materials via Reactive Molecular Dynamics Simulations.

We derive a database of atomistic structural models of amorphous carbon materials endowed with exohedral functional groups. We start from phases previously derived using the DynReaxMas method for reactive molecular dynamics simulations, which exhibit atomistic and medium-length-scale features in excellent agreement with available experimental data. Given a generic input structure/phase, we develop postprocessing simulation algorithms mimicking experimental preparation protocols aimed at: (1) "curing" the phase to decrease the defect concentration; (2) automatically selecting the most reactive carbon atoms via interaction with a probe molecular species, and (3) stabilizing the phase by saturating the valence of carbon atoms with single-bond functional groups. Although we focus on oxygen-bearing functionalities, they can be replaced with other monovalent groups, such as -H, -COOH, -CHO, so that the protocol is quite general. We finally classify reactive sites in terms of their location within the structural framework and coordination environment (edges, tunnels, rings, aromatic carbons becoming aliphatic) and try to single out descriptors that correlate with tendency to functionalization.

Structural basis for linker histone H5–nucleosome binding and chromatin fiber compaction

The hierarchical packaging of chromatin fibers plays a critical role in gene regulation. The 30-nm chromatin fibers, a central-level structure bridging nucleosomal arrays to higher-order organizations, function as the first level of transcriptional dormant chromatin. The dynamics of 30-nm chromatin fiber play a crucial role in biological processes related to DNA. Here, we report a 3.6-angstrom resolution cryogenic electron microscopy structure of H5-bound dodecanucleosome, i.e., the chromatin fiber reconstituted in the presence of linker histone H5, which shows a two-start left-handed double helical structure twisted by tetranucleosomal units. An atomic structural model of the H5-bound chromatin fiber, including an intact chromatosome, is built, which provides structural details of the full-length linker histone H5, including its N-terminal domain and an HMG-motif-like C-terminal domain. The chromatosome structure shows that H5 binds the nucleosome off-dyad through a three-contact mode in the chromatin fiber. More importantly, the H5-chromatin structure provides a fine molecular basis for the intra-tetranucleosomal and inter-tetranucleosomal interactions. In addition, we systematically validated the physiological functions and structural characteristics of the tetranucleosomal unit through a series of genetic and genomic studies in Saccharomyces cerevisiae and in vitro biophysical experiments. Furthermore, our structure reveals that multiple structural asymmetries of histone tails confer a polarity to the chromatin fiber. These findings provide structural and mechanistic insights into how a nucleosomal array folds into a higher-order chromatin fiber with a polarity in vitro and in vivo.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Atomic structure, electron-impact excitation and collisional-radiative modelling for Ar II

The spectra from singly ionized argon Ar II has significant diagnostic capability in the characterization and modelling of both magnetically-confined fusion and astrophysical plasmas. The literature has several pre-existing data sets for Ar+ but this paper presents the results from 3 new atomic structure and electron-impact scattering models in order to better constrain the differences in atomic data and how they impact well-known plasma diagnostics. Several independent atomic structure methodologies are employed to calculate the energy levels and transition probabilities for each model. The first approach employs a relativistic Dirac–Coulomb Hamiltonian model, the second approach uses a semi-relativistic Breit–Pauli Hamiltonian with the mass-velocity, Darwin and spin–orbit corrections, and in a third case an ICFT approach. Three atomic structure models provide a foundation for Dirac R-matrix, a semi-relativistic ICFT (Intermediate Coupling Frame Transformation) and a Breit–Pauli R-Matrix with Pseudostates (BPRMPS) calculation. Synthetic spectra utilizing these three data sets are compared against measurements taken at the Compact Toroidal Hybrid (CTH) stellarator, and the total radiative power loss is also benchmarked against previous calculations.

Crystal structure and shape selection in the growth of 3D metallic crystallites on layered materials: Fe on MoS2

Nucleation and growth of supported 3D metal clusters or crystallites during deposition on MoS2, or on other weakly-adhering layered materials, can potentially produce diverse growth shapes, and even crystal structures differing from the bulk metal. For Fe deposition on MoS2, SEM and AFM observations reveal three distinct crystallite shapes. By comparison with atomistic structure models incorporating realistic Fe-MoS2 interface structures, we conclude that these are: triangular fcc(111) pyramids with sloped {100} side facets; bcc(110) A-frame tents with sloped {100} side facets; and bcc(110) mesas with vertical {100} and {110} side facets. The following picture is proposed for the competitive formation of clusters and crystallites with different structures: (i) small nanoclusters formed at the onset of deposition exhibit facile fluxional dynamics allowing sampling of different crystal structures and shapes; (ii) sufficient fluxionality implies a Boltzmann distribution of sampled structures, and thus coexistence of different structures follows from the demonstrated similar energies for those structures; (iii) growing clusters reach a threshold size above which the characteristic time scale for restructuring exceeds that for cluster growth. Thereafter, clusters are locked-in to a specific crystal structure and shape as revealed by imaging of larger crystallites. Despite a penalty for fcc(111) over bcc(111) pyramids based on bulk energetics, favorable surface and interface energies makes them preferable for smaller sizes.

Orientation Relationship of Intergrowth Al2Fe and Al5Fe2 Intermetallics Determined by Single-Crystal X-ray Diffraction

Although the Al2Fe phase has similar decagonal-like atomic arrangements as that of the orthorhombic Al5Fe2 phase, no evidence for intergrowth samples of Al2Fe and Al5Fe2 has been reported. In the present work, the co-existence of Al2Fe and Al5Fe2 phases has been discovered from the educts obtained with a nominal atomic ratio of Al:Fe of 2:1 by arc melting. First, single-crystal X-ray diffraction (SXRD) as well as scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectroscopy (EDX) measurements have been utilized to determine the exact crystal structures of both phases, which are refined to be Al12.48Fe6.52 and Al5.72Fe2, respectively. Second, the orientation relationship between Al2Fe and Al5Fe2 has been directly deduced from the SXRD data sets, and the co-existence structure model has been constructed. Finally, four pairs of parallel atomic planes and their unique orientation relations have been determined from the reconstructed reciprocal-space precession images of (0kl), (h0l), and (hk0) layers. In addition, one kind of interface atomic structure model is constructed by the orientation relations between two phases, correspondingly.

Targeted optimization in small-scale atomic structure calculations: application to Au I

The lack of reliable atomic data can be a severe limitation in astrophysical modelling, in particular of events such as kilonovae that require information on all neutron-capture elements across a wide range of ionization stages. Notably, the presence of non-orthonormalities between electron orbitals representing configurations that are close in energy can introduce significant inaccuracies in computed energies and transition probabilities. Here, we propose an explicit targeted optimization (TO) method that can effectively circumvent this concern while retaining an orthonormal orbital basis set. We illustrate this method within the framework of small-scale atomic structure models of Au I, using the Grasp2018 multiconfigurational Dirac–Hartree–Fock atomic structure code. By comparing to conventional optimization schemes we show how a TO approach improves the energy level positioning and ordering. TO also leads to better agreement with experimental data for the strongest E1 transitions. This illustrates how small-scale models can be significantly improved with minor computational costs if orbital non-orthonormalities are considered carefully. These results should prove useful to multi-element atomic structure calculations in, for example, astrophysical opacity applications involving neutron-capture elements.

Dual-cluster interpretation of Au–Sn binary eutectics and solders

Au–Sn alloy is an important high-temperature solder, but it has some disadvantages such as high cost and brittleness. Multicomponent alloying is often performed to improve performance and reduce their cost. However, due to the absence of an atomic structure model, there has been a lack of effective theory to guide their composition design. Since Au–Sn solders are typically eutectic-based, understanding the Au–Sn eutectic at the atomic level is of great significance for clarifying the composition origin of Au–Sn solders and the subsequent multi-component composition design. In the present work, the short-range order of Au–Sn eutectics is characterized using a dual-cluster model. In the dual-cluster formulism, the two eutectics Au69.6Sn30.3 and Sn94.6Au5.4 at. % are interpreted in terms of the hypoeutectic [SnAu12]Sn2Au3 + [Au–Au2Sn6]Au3 = Au70.0Sn30.0 and [Au–Sn8]Au1 + 2{[Sn–Sn10]Sn5} = Sn95.2Au4.8 alloys, respectively. The compositions of Au–Sn solders are then analyzed based on the interpreted dual-cluster formulas, which indicate that the number of atoms of the alloying elements that replace the atoms in the dual-cluster formulas is always an integer. The present method provides a quantitative approach toward developing a practical composition interpretation and design tool for Au–Sn-based solders.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Spike Protein Variants.

The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development.

Surface structural analysis of SrF2(111) using low-energy atom scattering spectroscopy

We studied the surface structure of SrF2(111) using low-energy atom scattering spectroscopy. For the spectroscopy, we used pulsed 3 keV 4He° and 3 keV 20Ne0 beams as incident particles and detected 180° backscattered particles using a microchannel plate. We compared the experimental results with simulations employing three types of surface atomic structural models: F–Sr–F, Sr–F–F, and F–F–Sr Our results demonstrate that the topmost layer of SrF2(111) has approximately 60% and 40% of F–Sr–F and Sr–F–F, respectively.

Determination of the radiative transition rates in Ta VII using a multiplatform approach

Tantalum (Z=73) is an element that is produced in the neutron-induced transmutation of tungsten (Z=74) which in turn will compose the divertors in Tokamaks. As a consequence, their sputtering may generate ionic impurities of all possible charge states in the deuterium-tritium plasma that could contribute to radiation losses in controlled nuclear devices. The radiative properties of these ions have therefore potential important applications in this field. In this context, a multiplatform approach has been adopted in order to compute the Ta VII radiative rates and estimate their accuracy. The oscillator strengths and transition probabilities have been calculated for the 237 electric dipole (E1) transitions of the Ta VII spectrum as classified by Ryabtev et al. (2014). Three independent atomic structure models have been used; one based on the semi-empirical pseudo-relativistic Hartree–Fock (HFR) method and two based on the fully relativistic ab initio multiconfiguration Dirac–Hartree–Fock (MCDHF) method.

Undergraduate students’ models of single- and multi-electron atoms

ABSTRACT Quantum physics forms the basis for exciting new technologies, including quantum computers, quantum encryption, and quantum entanglement. The advancement of science and technology highlights the importance of mastering quantum physics and its applications, not only at the college level but also as early as high school. In this multiple case study, we investigated first- and second-year undergraduate college students’ models of single and multi-electron atoms after completing a modern mechanics course, which addressed basic QM topics. The students’ models were categorised into four primary categories: the discrete entity (particle) model, hybrid model, quantum-like model, and quantum model, and subcategories for atom structure and electron attribution and motion. Despite being introduced to quantum concepts in high school and first-year undergraduate calculus-based physics classes, many students constructed incomplete, inaccurate, or incoherent models. Seven of eight students’ atomic structure models and six of eight students’ electron attribution and motion models were classified as discrete entity and hybrid models. The findings of this study reveal students’ conceptual challenges in explaining the structure of single and multiple-electron atoms and shed light on the factors contributing to the persistent difficulties in their understanding of atomic structure in the realm of quantum mechanics.

Blue phosphorene on Au(111): theoretical, spectroscopic and diffraction analysis reveal the role of single Au adatoms.

In investigating the monoatomic layers of P, several stable two-dimensional (2D) allotropes have been theoretically predicted. Among them, single-layer blue phosphorus (BlueP) appears to deliver promising properties. After initial success, where the structure of BlueP triangular patches on Au(111) was conceived on the basis of scanning tunneling microscopy (STM) and density functional theory (DFT), the surface structure model was revisited multiple times with increasing accuracy and insight of theoretical calculations and experimental datasets. Interestingly, the quest for a reliable atomic structure model of BlueP on Au(111) turned out to be very contentious and challenging, particularly considering the possible incorporation of Au atoms in the 2D sheet of P. This article proposes an extended report on theoretical findings that can be extracted from DFT calculations of the orbital projected band structure and employed for an efficient comparison protocol between the calculations and experimental datasets obtained from angle-resolved photoemission spectroscopy (ARPES). The findings, together with experimental and simulated data from STM imaging and surface X-ray diffraction (SXRD), show a clear way to verify the presence and characterize the stabilizing effect of foreign atoms in 2D materials.

Observation of Bacteriophage Ultrastructure by Cryo-Electron Microscopy.

Transmission electron microscopy (TEM) is an ideal method to observe and determine the structure of bacteriophages. From early studies by negative staining to the present atomic structure models derived from cryo-TEM, bacteriophage detection, classification, and structure determination have been mostly done by electron microscopy. Although embedding in metal salts has been a routine method for virus observation for many years, the preservation of bacteriophages in a thin layer of fast frozen buffer has proven to be the most convenient preparation method for obtaining images using cryo-electron microscopy (cryo-EM). In this technique, frozen samples are observed at liquid nitrogen temperature, and the images are acquired using different recording media. The incorporation of direct electron detectors has been a fundamental step in achieving atomic resolution images of a number of viruses. These projection images can be numerically combined using different approaches to render a three-dimensional model of the virus. For those viral components exhibiting any symmetry, averaging can nowadays achieve atomic structures in most cases. Image processing methods have also evolved to improve the resolution in asymmetric viral components or regions showing different types of symmetries (symmetry mismatch).

Multilevel atomic structural model for interstratified opal materials

The structure of opal has long fascinated scientists. It occurs in a number of structural states, ranging from amorphous to exhibiting features of stacking disorder. Opal-CT, where C and T signify cristobalite- and tridymite-like interstratification, represents an important link in the length scales between amorphous and crystalline states. However, details about local atomic (dis)order and arrangements extending to long-range stacking faults in opal polymorphs remain incompletely understood. Here, a multilevel modeling approach is reported that considers stacking states in correlation with the abundance of C and T segments as a high-level structural parameter (i.e. not each atom). Optimization accounting for inter-tetrahedral bond lengths and angles and the regularity of the silicate tetrahedra is included as lower levels of structural parameters. Together, a set of parameters with both coarse-grained and atomistic features for different levels of structural details is refined. Structural disorder at the ∼10–100 Å distance scale is evaluated using experimental pair distribution function and diffraction datasets, comparing peak intensities, widths and asymmetry. This work presents a complete multilevel structural description of natural opal-CT and explains many of the unusual features observed in X-ray powder diffraction patterns. This modeling approach can be adopted generally for analyzing layered materials and their assembly into 3D structures.

Effect of data quality on results of Reverse Monte Carlo analysis of EXAFS data

Reverse Monte Carlo (RMC) is a computational technique used to generate three-dimensional atomic structural models compatible with a given set of experimental data. RMC is applicable to X-ray absorption fine structure (XAFS) data, which generally show different energy ranges and noise levels depending on the experimental conditions. In this article, we explore the relationship between these effects and the precision of structural results obtained through RMC, by examining two simple molecular cases. In such specific cases (Br2 and BBr3) we have found that the XAFS data range has generally a limited influence on the structural results, except for signals on restricted wave-vector ranges (kmax<8A−1). Different noise levels are shown to affect the precision of the structural results, depending also on how the noise is included in the RMC procedure. As an outcome of this study, we propose general guidelines for best practices in RMC XAFS data-analysis, aiming to improve the accuracy and reliability of atomic structure modeling.

Unraveling the atomic structure of the R(15×12) reconstruction of carburized W(110) based on ab initio calculations

Carburized W(110) surfaces exhibit a nm-scale rectangular periodicity on their surface, thus providing an excellent platform for the assembly of self-organized nanostructures, i.e., nano-patterning. Understanding their atomic structure and surface properties is essential for their further exploitation in this direction. Two types of such surface reconstructions have been observed, namely the R(15 × 3) and the R(15 × 12). Although the atomic structure of the R(15 × 3) surface has been recently discovered, the one of the R(15 × 12) remains unknown. In this work, we propose a new atomic structure model for the R(15 × 12) surface. We then employ density functional theory and machine learning based molecular dynamics calculations to show that the proposed model is consistent with the experimental scanning tunneling microscopy imaging of the surfaces.

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.

DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid structures and their complexes with proteins are determined by cryogenic electron microscopy (cryo-EM), structure modeling for DNA and RNA remains challenging particularly when the map is determined at a resolution coarser than atomic level. Moreover, computational methods for nucleic acid structure modeling are relatively scarce. Here, we present CryoREAD, a fully automated de novo DNA/RNA atomic structure modeling method using deep learning. CryoREAD identifies phosphate, sugar and base positions in a cryo-EM map using deep learning, which are traced and modeled into a three-dimensional structure. When tested on cryo-EM maps determined at 2.0 to 5.0 Å resolution, CryoREAD built substantially more accurate models than existing methods. We also applied the method to cryo-EM maps of biomolecular complexes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Defect graph neural networks for materials discovery in high-temperature clean-energy applications.

We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. It will help to tackle future materials discovery problems in clean energy and beyond.