Determination Of Atomic Structure Research Articles

Molecular architecture of the assembly of Bacillus spore coat protein GerQ revealed by cryo-EM

Protein filaments are ubiquitous in nature and have diverse biological functions. Cryo-electron microscopy (cryo-EM) enables the determination of atomic structures, even from native samples, and is capable of identifying previously unknown filament species through high-resolution cryo-EM maps. In this study, we determine the structure of an unreported filament species from a cryo-EM dataset collected from Bacillus amyloiquefaciens biofilms. These filaments are composed of GerQ, a spore coat protein known to be involved in Bacillus spore germination. GerQ assembles into a structurally stable architecture consisting of rings containing nine subunits, which stacks to form filaments. Molecular dockings and model predictions suggest that this nine-subunit structure is suitable for binding CwlJ, a protein recruited by GerQ and essential for Ca2+-DPA induced spore germination. While the assembly state of GerQ within the spores and the direct interaction between GerQ and CwlJ have yet to be validated through further experiments, our findings provide valuable insights into the self-assembly of GerQ and enhance our understanding of its role in spore germination.

3D atomic structure from a single X-ray free electron laser pulse

X-ray Free Electron Lasers (XFEL) are cutting-edge pulsed x-ray sources, whose extraordinary pulse parameters promise to unlock unique applications. Several new methods have been developed at XFELs; however, no methods are known, which allow ab initio atomic level structure determination using only a single XFEL pulse. Here, we present experimental results, demonstrating the determination of the 3D atomic structure from data obtained during a single 25 fs XFEL pulse. Parallel measurement of hundreds of Bragg reflections was done by collecting Kossel line patterns of GaAs and GaP. To the best of our knowledge with these measurements, we reached the ultimate temporal limit of the x-ray structure solution possible today. These measurements open the way for obtaining crystalline structures during non-repeatable fast processes, such as structural transformations. For example, the atomic structure of matter at extremely non-ambient conditions or transient structures formed in irreversible physical, chemical, or biological processes may be captured in a single shot measurement during the transformation. It would also facilitate time resolved pump-probe structural studies making them significantly shorter than traditional serial crystallography.

Metallo-supramolecular branched polymer protects particles from air-water interface in single-particle cryo-electron microscopy

Recent technological breakthroughs in single-particle cryo-electron microscopy (cryo-EM) enable rapid atomic structure determination of biological macromolecules. A major bottleneck in the current single particle cryo-EM pipeline is the preparation of good quality frozen cryo-EM grids, which is mostly a trial-and-error process. Among many issues, preferred particle orientation and sample damage by air–water interface (AWI) are common practical problems. Here we report a method of applying metallo-supramolecular branched polymer (MSBP) in the cryo-sample preparation for high-resolution single-particle cryo-EM. Our data shows that MSBP keeps a majority of particles away from air–water interface and mitigates preferred orientation as verified by the analyses of apoferritin, hemagglutinin) trimer and various sample proteins. The use of MSBP is a simple method to improve particle distribution for high-resolution structure determination in single-particle cryo-EM.

Atomic-level imaging of beam-sensitive COFs and MOFs by low-dose electron microscopy.

Electron microscopy, an important technique that allows for the precise determination of structural information with high spatiotemporal resolution, has become indispensable in unravelling the complex relationships between material structure and properties ranging from mesoscale morphology to atomic arrangement. However, beam-sensitive materials, particularly those comprising organic components such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), would suffer catastrophic damage from the high energy electrons, hindering the determination of atomic structures. A low-dose approach has arisen as a possible solution to this problem based on the integration of advancements in several aspects: electron optical system, detector, image processing, and specimen preservation. This article summarizes the transmission electron microscopy characterization of MOFs and COFs, including local structures, host-guest interactions, and interfaces at the atomic level. Revolutions in advanced direct electron detectors, algorithms in image acquisition and processing, and emerging methodology for high quality low-dose imaging are also reviewed. Finally, perspectives on the future development of electron microscopy methodology with the support of computer science are presented.

Active learning prediction and experimental confirmation of atomic structure and thermophysical properties for liquid Hf_{76}W_{24} refractory alloy.

The determination of liquid atomic structure and thermophysical properties is essential for investigating the physical characteristics and phase transitions of refractory alloys. However, due to the stringent experimental requirements and underdeveloped interatomic potentials, acquiring such information through experimentation or simulation remains challenging. Here, an active learning method incorporating a deep neural network was established to generate the interatomic potential of the Hf_{76}W_{24} refractory alloy. Then the achieved potential was applied to investigate the liquid atomic structure and thermophysical properties of this alloy over a wide temperature range. The simulation results revealed the distinctive bonding preferences among atoms, that is, Hf atoms exhibited a strong tendency for conspecific bonding, while W atoms preferred to form an interspecific bonding. The analysis of short-range order (SRO) in the liquid alloy revealed a significant proportion of icosahedral (ICO) and distorted ICO structures, which even exceeded 30% in the undercooled state. As temperature decreased, SRO structures demonstrated an increase in larger coordination number (CN) clusters and a decrease in smaller CNs. The alterations of the atomic structure indicated that the liquid alloy becomes more ordered, densely packed, and energetically favorable with decreasing temperature, consistent with the obtained fact: Both density and surface tension increase linearly. The simulated thermophysical properties were close to experimental values with minor deviations of 2.8% for density and 3.4% for surface tension. The consistency of the thermophysical properties further attested to the accuracy and reliability of active learning simulation.

In situ preparation and determination of CeO2 high-index surface atomic structures

Understanding the properties regarding the high-index surfaces of the oxide nanocrystals is of great importance, but it remains challenging to obtain atomic-level information due to the lack of efficient preparation methods for high-index surfaces. Herein, we presented a work to in situ prepare and determine the intrinsic atomic structures of nanocrystalline CeO2 high-index surfaces via in situ spherical aberration (Cs)-corrected scanning transmission electron microscopy (STEM). By utilizing a reshaping process driven by the Wulff reconstruction, high-index surfaces of CeO2 nanocrystals including {210} {311}, and {533} were successfully prepared. Combined STEM with the density functional theory calculations, these high-index surface structures were determined with atomic precision, and interestingly {533} exhibited a unique atomic pit feature with low-coordinated Ce atoms exposure and unique electrical properties. This work has revealed new information about CeO2 high-index surfaces through experiments, enhancing our understanding of these surfaces, and also providing a new route for preparing high-index surfaces.

“Seeing” Individual Atoms in 3D: Atomic Electron Tomography

Have you ever seen atoms in 3D? Atoms are the basic building blocks of materials, and scientists have been actively seeking ways to “see” the 3-dimensional positions of atoms, i.e., where the atoms are located within materials. With the development of powerful modern transmission electron microscopy technology and advanced reconstruction algorithms, it became possible to really observe the individual atoms in 3D. In this paper, we will review the recent progress of the technique “atomic electron tomography”, which allowed the 3D atomic structural determination of complex nanomaterials.

Multislice Electron Tomography Using Four-Dimensional Scanning Transmission Electron Microscopy

Electron tomography offers useful three-dimensional (3D) structural information, which cannot be observed by two-dimensional imaging. By combining annular dark-field scanning transmission electron microscopy (ADF STEM) with aberration correction, the resolution of electron tomography has reached atomic resolution. However, tomography based on ADF STEM inherently suffers from several issues, including a high electron-dose requirement, poor contrast for light elements, and artifacts from image-contrast nonlinearity. Here, we develop an alternative method called multislice electron tomography (MSET) based on four-dimensional STEM tilt series. Our simulations show that multislice-based 3D reconstruction can effectively reduce undesirable reconstruction artifacts from the nonlinear contrast, allowing precise determination of atomic structures with improved sensitivity for low-Z elements, at considerably low electron-dose conditions. We expect that the MSET method can be applied to a wide variety of materials, including radiation-sensitive samples and materials containing light elements whose 3D atomic structures have never been fully elucidated due to electron-dose limitations or nonlinear imaging contrast.

Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at https://zenodo.org/record/7273314.

Towards quantitative determination of atomic structures of amorphous materials in three dimensions

Amorphous materials have broad applications ranging from daily life to extreme conditions due to their unique properties in elasticity, strength and resistivity. However, experimentally determining the three-dimensional (3D) atomic structure of amorphous materials has been a long-standing problem. Atomic resolution electron tomography (AET) has proved an increasingly powerful tool for atomic scale structural characterization without any crystalline assumptions. In this review, we summarize the state-of-art characterization methods for the exploration of atomic structures of amorphous materials in the past few decades. We introduce the principles and recent progresses of AET, and highlight the first groundbreaking experimental determination of all 3D atomic positions in amorphous solids. We also discuss the new opportunities and challenges for characterizing the chemical and structural defects in amorphous materials.

Investigating ultrafast structural dynamics using high repetition rate x-ray FEL radiation at European XFEL

European XFEL is an international facility providing hard and soft x-ray free-electron laser radiation for user experiments with a wide range of scientific applications. Its superconducting linear accelerator enables high repetition rate experiments with a broad range of x-ray pulse delivery patterns. The combination of time-resolved experiments, providing access to the time-domain from sub-femtoseconds to milliseconds, with atomic resolution x-ray geometric and electronic structure determination methods is responsible for the bulk of scientific applications of European XFEL. In addition, the extreme x-ray intensities and coherence properties open new methods for studying matter out of equilibrium. After start of operation in 2017, the facility now harvests scientific applications with impact to the challenge areas climate and energy, health, environment and sustainability, and digitalization. Extensions of European XFEL aim to increase performance and capabilities for new scientific applications. An upgrade of the facility in the early 2030s will increase the applicability of European XFEL to solid materials and provide dedicated instruments for improved conditions in specific research fields.

Diversity and molecular mechanisms of channelrhodopsins.

Channelrhodopsins (ChRs) are light-gated channels that guide phototaxis in eukaryotic microorganisms and are widely used as molecular tools to control cell membrane potential with light (optogenetics). Expression of ChR genes in neurons and cardiomyocytes enables photoactivation or photoinhibition of these cells’ activity with submillisecond precision. Recent advances in polynucleotide sequencing have led to identification of hundreds of ChR homologs in many phylogenetic lineages, but only a few have been characterized functionally. ChRs differ in their ionic selectivity, spectral sensitivity, and photocurrent kinetics and thus provide an exceptional platform for the study of structure-function relationships in membrane proteins. Current evidence suggests that ion channel function has evolved within the superfamily of microbial rhodopsins by several convergent routes. Our laboratory has discovered natural ChR variant families with major different biophysical properties, and we are studying their photochemical mechanisms by atomic structure determination, electrophysiology, kinetic spectroscopy, and structure/function analysis by mutagenesis. Also, we apply molecular engineering to improve their biophysical properties to suit practical needs of optogenetics. Recently we identified potassium-selective ChRs in non-photosynthetic stramenopile and alveolate microbes and demonstrated the utility of these molecules as inhibitory optogenetic tools. These proteins are promising for research and potentially for treatment of potassium channelopathies such as epilepsy, Parkinson's disease, and cardiac arrhythmias.

Method for 3D atomic structure determination of multi-element nanoparticles with graphene liquid-cell TEM

Determining the 3D atomic structures of multi-element nanoparticles in their native liquid environment is crucial to understanding their physicochemical properties. Graphene liquid cell (GLC) TEM offers a platform to directly investigate nanoparticles in their solution phase. Moreover, exploiting high-resolution TEM images of single rotating nanoparticles in GLCs, 3D atomic structures of nanoparticles are reconstructed by a method called “Brownian one-particle reconstruction”. We here introduce a 3D atomic structure determination method for multi-element nanoparticle systems. The method, which is based on low-pass filtration and initial 3D model generation customized for different types of multi-element systems, enables reconstruction of high-resolution 3D Coulomb density maps for ordered and disordered multi-element systems and classification of the heteroatom type. Using high-resolution image datasets obtained from TEM simulations of PbSe, CdSe, and FePt nanoparticles that are structurally relaxed with first-principles calculations in the graphene liquid cell, we show that the types and positions of the constituent atoms are precisely determined with root mean square displacement values less than 24 pm. Our study suggests that it is possible to investigate the 3D atomic structures of synthesized multi-element nanoparticles in liquid phase.

Electron density mapping of boron clusters via convolutional neural networks to augment structure prediction algorithms.

Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster are mainly governed by its molecular structure. Traditionally, structure elucidation is achieved using quantum mechanics (QM) based calculations that are usually tedious and time consuming for large nanoclusters. Various structural prediction algorithms have been reported in the literature (CALYPSO, USPEX). Although they tend to accelerate the structure exploration, they still require the aid of QM based calculations for structure evaluation. This makes the structure prediction process quite a computationally expensive affair. In this paper, we report on the creation of a convolutional neural network model, which can give relatively accurate energies for the ground state of nanoclusters from the promolecule density on the fly and could thereby be utilized for aiding structure prediction algorithms. We tested our model on dataset consisting of pure boron nanoclusters of varying sizes.

Atomic resolution structure determination of larger macromolecular complexes by cryo-EM and X-ray crystallography

Atomic resolution structure determination of larger macromolecular complexes by cryo-EM and X-ray crystallography

Toward Low-Symmetry Systems: An Adaptive Differential Evolution Algorithm for Global Structure Searching.

The reduction in the symmetry of nanomaterials can produce unexpected properties, while the determination of atomic structures is a sizable challenge in related fields, including low-dimensional materials, surface science, defects, etc. Herein, we develop an adaptive algorithm based on the differential evolution algorithm, which provides benefits for structure searching on low-symmetry systems. The dynamic strategy pool and the island concept are proposed to accelerate the efficiency in the full search space. With several test examples, the designed program not only locates reported structures but also affords new stable configurations that were not located by previous structure search algorithms. Moreover, we provide frameworks and interfaces for stable structure searching on complex systems like grain boundaries, supported clusters, surfaces, and edges. The success in repeatable structure searching with high efficiency demonstrates the reliability and practicability of our algorithm and ensures its potential applications as an advanced technology in many newly arising fields.

The FUSION protein crystallization screen.

The success and speed of atomic structure determination of biological macromolecules by X-ray crystallography depends critically on the availability of diffraction-quality crystals. However, the process of screening crystallization conditions often consumes large amounts of sample and time. An innovative protein crystallization screen formulation called FUSION has been developed to help with the production of useful crystals. The concept behind the formulation of FUSION was to combine the most efficient components from the three MORPHEUS screens into a single screen using a systematic approach. The resulting formulation integrates 96 unique combinations of crystallization additives. Most of these additives are small molecules and ions frequently found in crystal structures of the Protein Data Bank (PDB), where they bind proteins and complexes. The efficiency of FUSION is demonstrated by obtaining high yields of diffraction-quality crystals for seven different test proteins. In the process, two crystal forms not currently in the PDB for the proteins α-amylase and avidin were discovered.

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions.

The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO x overlayer structures on SrTiO3(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO3(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions.

Application of super-resolution and correlative double sampling in cryo-electron microscopy.

Developments in cryo-EM have allowed atomic or near-atomic resolution structure determination to become routine in single particle analysis (SPA). However, near-atomic resolution structures determined using cryo-electron tomography and sub-tomogram averaging (cryo-ET STA) are much less routine. In this paper, we show that collecting cryo-ET STA data using the same conditions as SPA, with both correlated double sampling (CDS) and the super-resolution mode, allowed apoferritin to be reconstructed out to the physical Nyquist frequency of the images. Even with just two tilt series, STA yields an apoferritin map at 2.9 Å resolution. These results highlight the exciting potential of cryo-ET STA in the future of protein structure determination. While processing SPA data recorded in super-resolution mode may yield structures surpassing the physical Nyquist limit, processing cryo-ET STA data in the super-resolution mode gave no additional resolution benefit. We further show that collecting SPA data in the super-resolution mode, with CDS activated, reduces the estimated B-factor, leading to a reduction in the number of particles required to reach a target resolution without compromising the data size on disk and the area imaged in SerialEM. However, collecting SPA data in CDS does reduce throughput, given that a similar resolution structure, with a slightly larger B-factor, is achievable with optimised parameters for speed in EPU (without CDS).

High-throughput cryo-EM structure determination of amyloids.

The formation of amyloid filaments is characteristic of various degenerative diseases. Recent breakthroughs in electron cryo-microscopy (cryo-EM) have led to atomic structure determination of multiple amyloid filaments, both of filaments assembled in vitro from recombinant proteins, and of filaments extracted from diseased tissue. These observations revealed that a single protein may adopt multiple different amyloid folds, and that in vitro assembly does not necessarily lead to the same filaments as those observed in disease. In order to develop relevant model systems for disease, and ultimately to better understand the molecular mechanisms of disease, it will be important to determine which factors determine the formation of distinct amyloid folds. High-throughput cryo-EM, in which structure determination becomes a tool rather than a project in itself, will facilitate the screening of large numbers of in vitro assembly conditions. To this end, we describe a new filament picking algorithm based on the Topaz approach, and we outline image processing strategies in Relion that enable atomic structure determination of amyloids within days.