Structure Determination Methods Research Articles

A noise-robust data assimilation method for crystal structure determination using powder diffraction intensity.

Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder x-ray diffraction (XRD) data are helpful in a crystal structure search using simulated annealing, even when they are insufficient for structure determination by themselves [Tsujimoto et al., Phys. Rev. Mater. 2, 053801 (2018)]. In the method, the XRD data are assimilated into the simulation by adding a penalty function to the physical potential energy, where a crystallinity-type penalty function, defined by the difference between experimental and simulated diffraction angles was used. To improve the success rate and noise robustness, we introduce a correlation-coefficient-type penalty function adaptable to XRD data with significant experimental noise. We apply the new penalty function to SiO2 coesite and ɛ-Zn(OH)2 to determine its effectiveness in the data assimilation method.

Experimental study on flexural behavior of damaged concrete beams strengthened with high ductility concrete under repeated load

In this study, pre-damaged beams were repaired and strengthened using high ductility concrete (HDC), and the extent of damage at different stages of the beam was measured using an ultrasound method. Nine concrete beams were fabricated, one of which was a control beam and the rest were HDC-reinforced beams. The investigated parameter was the thickness of the HDC reinforcement layers, and four thickness values were investigated. Damage equations and thresholds were obtained on the basis of damage mechanics and probabilistic statistical analyses. The results showed that as the thickness of the reinforcement layer increased, the ultimate load-carrying capacity and cracking load of the reinforced beam gradually increased while the residual deformation gradually decreased. Compared with the load-carrying capacity of the test beam in Group A, that of the test beam in Group D increased by 46.3%, the residual deformation reduced by 32.5%, and the cracking load increased by 55.7%. The comparison between beams in Groups A and D was performed in terms of ultimate load-carrying capacity, crack control capacity, and degree of damage. The comparison showed that as the thickness of the reinforcement layer increased, the load-carrying capacity of the structure increased while the degree of damage decreased; however, this behavior was not suitable for the healing of the structural cracks. Therefore, the reinforcement measures and structural damage determination methods proposed in this study are simple and convenient, easy to deploy on a large scale, and valuable to the further development of the industry.

Overview of research and development of polypeptide drugs and traditional Chinese medicine-peptides

Peptide is a compound consisting of 2-50 amino acids, which is intermediate between small molecule and protein. It is characterized by a variety of biological activities, easy absorption, strong specific targeting, and few side effects and has become one of the hotspots in biomedical research in recent years. Chinese medicine contains a large number of peptides. The traditional processing methods such as decocting and boiling can effectively boost peptides to exert their due biological activities. At present, however, the research on Chinese medicinal components in laboratory generally employs high-concentration alcohol extraction method, which may cause the peptides to be ignored in many natural Chinese medicines. Substantial studies have revealed that the peptides in Chinese medicine are important material basis responsible for the traditional efficacy. Based on years of research and literature retrieval, this study put forward the concept of "traditional Chinese medicine(TCM)-peptides", referring to the components consisting of two or more amino acids with molecular weight between small molecules and proteins that can express the efficacy of Chinese medicine. Furthermore, this study also summarized the extraction and separation of TCM-peptides, and structure determination methods and routes, predicted the research prospect of modern research methods of TCM-peptides based on "holistic view" and big data. The artificial intelligence prediction was combined with high-throughput screening technology to improve the discovery efficiency and accuracy of TCM-peptides, and holographic images between TCM-peptides and biological targets were established to provide references for the innovative drug design and related health product development of TCM-peptides based on TCM theories.

Stochastic atomic modeling and optimization with fullrmc

Understanding materials' atomic structure with a high level of confidence and certainty is often regarded as a very arduous and sometimes impossible task, especially for newer, emerging technology materials exhibiting limited long-range order. Nevertheless, information about atomic structural properties is very valuable for materials science and synthesis. For non-crystalline amorphous and nanoscale materials, using conventional structural determination methods is impossible. Reverse Monte Carlo (RMC) modeling is commonly used to derive models of materials from experimental diffraction data. Here, the latest developments in the fullrmc software package are discussed. Despite its name, fullrmc provides a very flexible modeling framework for solving atomic structures with many methods beyond RMC. The stochastic nature of fullrmc allows it to explore all possible dimensions and degrees of freedom for atomic modeling and create statistical solutions to match measurements. Differing versions of fullrmc are provided as open source or for cloud computing access. The latter includes a modern web-based graphical user interface that incorporates advanced computing and structure-building modules and machine-learning-based components. The main features of fullrmc are presented, including constraint types, boundary conditions, density shape functions and the two running modes: stochastic using a Monte Carlo algorithm and optimization using a genetic algorithm. Capabilities include tools for statistical, mesoscopic and nanoscopic approaches, atomic or coarse-grained models, and smart artificial-intelligence-ready loss functions.

Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.

Cell-free protein crystallization for nanocrystal structure determination

In-cell protein crystallization (ICPC) has been investigated as a technique to support the advancement of structural biology because it does not require protein purification and a complicated crystallization process. However, only a few protein structures have been reported because these crystals formed incidentally in living cells and are insufficient in size and quality for structure analysis. Here, we have developed a cell-free protein crystallization (CFPC) method, which involves direct protein crystallization using cell-free protein synthesis. We have succeeded in crystallization and structure determination of nano-sized polyhedra crystal (PhC) at a high resolution of 1.80 Å. Furthermore, nanocrystals were synthesized at a reaction scale of only 20 μL using the dialysis method, enabling structural analysis at a resolution of 1.95 Å. To further demonstrate the potential of CFPC, we attempted to determine the structure of crystalline inclusion protein A (CipA), whose structure had not yet been determined. We added chemical reagents as a twinning inhibitor to the CFPC solution, which enabled us to determine the structure of CipA at 2.11 Å resolution. This technology greatly expands the high-throughput structure determination method of unstable, low-yield, fusion, and substrate-biding proteins that have been difficult to analyze with conventional methods.

Advances and opportunities in RNA structure experimental determination and computational modeling.

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.

Reliability analysis-based investigation of the historical Széchenyi Chain Bridge deck system

Before the recent reconstruction, significant corrosion damages were observed on the deck system of the ∼170-year-old Széchenyi Chain Bridge. Therefore, an advanced reliability analysis-based study is executed to assess the risk of failure of the structure in its situation until the refurbishment starts. The current paper has the aim to introduce the applied assessment method for the risk analysis and damage grade determination of the historical structure. The novel method combines the following techniques: (i) input data coming from on-site measurements implemented into state-of-the-art corrosion models, (ii) advanced finite element model-based resistance calculation (GMNI analysis) implemented into (iii) Monte Carlo simulation-based stochastic reliability assessment method to determine the risk of the bridge deck system failure. It is concluded that the introduced method is a powerful tool for risk assessment of existing aging structures.

A Novel Antimicrobial Metabolite Produced by Paenibacillus apiarius Isolated from Brackish Water of Lake Balkhash in Kazakhstan.

Four aerobic bacteria with bacteriolytic capabilities were isolated from the brackish water site Strait Uzynaral of Lake Balkhash in Kazakhstan. The morphology and physiology of the bacterial isolates have subsequently been analyzed. Using matrix assisted laser desorption ionization-time of flight mass spectrum and partial 16S rRNA gene sequence analyses, three of the isolates have been identified as Pseudomonas veronii and one as Paenibacillus apiarius. We determined the capability of both species to lyse pre-grown cells of the Gram-negative strains Pseudomonas putida SBUG 24 and Escherichia coli SBUG 13 as well as the Gram-positive strains Micrococcus luteus SBUG 16 and Arthrobacter citreus SBUG 321 on solid media. The bacteriolysis process was analyzed by creating growth curves and electron micrographs of co-cultures with the bacteriolytic isolates and the lysis sensitive strain Arthrobacter citreus SBUG 321 in nutrient-poor liquid media. One metabolite of Paenibacillus apiarius was isolated and structurally characterized by various chemical structure determination methods. It is a novel antibiotic substance.

Complementarity of neutron, XFEL and synchrotron crystallography for defining the structures of metalloenzymes at room temperature.

Room-temperature macromolecular crystallography allows protein structures tobe determined under close-to-physiological conditions, permits dynamic freedom in protein motions and enables time-resolved studies. In the case of metalloenzymes that are highly sensitive to radiation damage, such room-temperature experiments can present challenges, including increased rates of X-ray reduction of metal centres and site-specific radiation-damage artefacts, as well as in devising appropriate sample-delivery and data-collection methods. It can also be problematic to compare structures measured using different crystal sizes and light sources. In this study, structures of a multifunctional globin, dehaloperoxidase B (DHP-B), obtained using several methods of room-temperature crystallographic structure determination are described and compared. Here, data were measured from large single crystals and multiple microcrystals using neutrons, X-ray free-electron laser pulses, monochromatic synchrotron radiation and polychromatic (Laue) radiation light sources. These approaches span a range of 18 orders of magnitude in measurement time per diffraction pattern and four orders of magnitude in crystal volume. The first room-temperature neutron structures of DHP-B are also presented, allowing the explicit identification of the hydrogen positions. The neutron data proved to be complementary to the serial femtosecond crystallography data, with both methods providing structures free of the effects of X-ray radiation damage when compared with standard cryo-crystallography. Comparison of these room-temperature methods demonstrated the large differences in sample requirements, data-collection time and the potential for radiation damage between them. With regard to the structure and function of DHP-B, despite the results being partly limited by differences in the underlying structures, new information was gained on the protonation states of active-site residues which may guide future studies of DHP-B.

Semi-experimental equilibrium (re SE) and theoretical structures of hydrazoic acid (HN3).

Hydrazoic acid (HN3) is used as a case study for investigating the accuracy and precision by which a molecular structure-specifically, a semi-experimental equilibrium structure (re SE)-may be determined using current state-of-the-art methodology. The influence of the theoretical corrections for effects of vibration-rotation coupling and electron-mass distribution that are employed in the analysis is explored in detail. The small size of HN3 allowed us to deploy considerable computational resources to probe the basis-set dependence of these corrections using a series of coupled-cluster single, double, perturbative triple [CCSD(T)] calculations with cc-pCVXZ (X = D, T, Q, 5) basis sets. We extrapolated the resulting corrections to the complete basis set (CBS) limit to obtain CCSD(T)/CBS corrections, which were used in a subsequent re SE structure determination. The re SE parameters obtained using the CCSD(T)/cc-pCV5Z corrections are nearly identical to those obtained using the CCSD(T)/CBS corrections, with uncertainties in the bond distances and angles of less than 0.0006 Å and 0.08°, respectively. The previously obtained re SE structure using CCSD(T)/ANO2 agrees with that using CCSD(T)/cc-pCV5Z to within 0.000 08 Å and 0.016° for bond distances and angles, respectively, and with only 25% larger uncertainties, validating the idea that re SE structure determinations can be carried out with significantly smaller basis sets than those needed for similarly accurate, strictly ab initio determinations. Although the purely computational re structural parameters [CCSD(T)/cc-pCV6Z] fall outside of the statistical uncertainties (2σ) of the corresponding re SE structural parameters, the discrepancy is rectified by applying corrections to address the theoretical limitations of the CCSD(T)/cc-pCV6Z geometry with respect to basis set, electron correlation, relativity, and the Born-Oppenheimer approximation, thereby supporting the contention that the semi-experimental approach is both an accurate and vastly more efficient method for structure determinations than is brute-force computation.

Atomic-resolution structures from polycrystalline covalent organic frameworks with enhanced cryo-cRED

The pursuit of atomic precision structure of porous covalent organic frameworks (COFs) is the key to understanding the relationship between structures and properties, and further developing new materials with superior performance. Yet, a challenge of how to determine their atomic structures has always existed since the first COFs reported seventeen years ago. Here, we present a universal method for ab initio structure determination of polycrystalline three-dimensional (3D) COFs at atomic level using enhanced cryo-continuous rotation electron diffraction (cryo-cRED), which combines hierarchical cluster analysis with cryo-EM technique. The high-quality datasets possess not only up to 0.79-angstrom resolution but more than 90% completeness, leading to unambiguous solution and precise refinement with anisotropic temperature factors. With such a powerful method, the dynamic structures with flexible linkers, degree of interpenetration, position of functional groups, and arrangement of ordered guest molecules are successfully revealed with atomic precision in five 3D COFs, which are almost impossible to be obtained without atomic resolution structure solution. This study demonstrates a practicable strategy for determining the structures of polycrystalline COFs and other beam-sensitive materials and to help in the future discovery of novel materials on the other.

Microalgae-derived polysaccharides: Potential building blocks for biomedical applications

In recent years, the increasing concern about human health well-being has strongly boosted the search for natural alternatives that can be used in different fields, especially in biomedicine. This has put microalgae-based products in evidence since they contain many bioactive compounds, of which polysaccharides are attractive due to the diverse physicochemical properties and new or improved biological roles they play. Polysaccharides from microalgae, specially exopolysaccharides, are critically important for market purposes because they can be used as anti-inflammatory, immunomodulatory, anti-glycemic, antitumor, antioxidant, anticoagulant, antilipidemic, antiviral, antibacterial, and antifungal agents. Therefore, to obtain higher productivity and competitiveness of these naturally available compounds, the cultivation parameters and the extraction/purification processes must be better optimized in order to bring perspectives for the exploitation of products in commercial and clinical practice. In this sense, the objective of the present review is to elucidate the potential biomedical applications of microalgae-derived polysaccharides. A closer look is taken at the main polysaccharides produced by microalgae, methods of extraction, purification and structural determination, biological activities and their applications, and current status.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures

The application of machine learning (ML) to electronic structure theory enables electronic property prediction with ab initio accuracy. However, most previous ML models predict one or several properties of intrinsic materials. The prediction of electronic band structure, which embeds all the main electronic information, has yet to be deeply studied. This is a challenging task due to the highly variable inputs and outputs; the input materials may have different sizes and compositions, and the output band structures may have varying band numbers and k-point samplings. This task becomes even more difficult when quantum-confined nanostructures are considered, whose band structures are sensitive to the confinements applied. This paper presents an ML framework for predicting band structures of quantum-confined nanostructures from their geometries. Our framework introduces a graph convolutional network applicable to materials with varying compositions and geometries to extract their atoms’ local environment information. A learnable real-space Hamiltonian construction process then enables the utilization of the information to predict the electronic structure at any arbitrary k-point; the theoretical foundations introduced in this process help to capture and incorporate minor changes in quantum confinements into band structures, and endow the framework with the ability of few-shot learning. Taking an example of graphene nanoribbons, typical quantum-confined nanostructures, we show how the framework is constructed and its excellent performance on band structure prediction with a tiny data set. Our framework may not only provide a rapid yet reliable method for electronic structure determination but also enlighten the applications of graph representation to ML in related fields.

Identification of flexible Pif1-DNA interactions and their impacts on enzymatic activities.

Flexible regions in biomolecular complexes, although crucial to understanding structure–function relationships, are often unclear in high-resolution crystal structures. In this study, we showed that single-molecule techniques, in combination with computational modeling, can characterize dynamic conformations not resolved by high-resolution structure determination methods. Taking two Pif1 helicases (ScPif1 and BsPif1) as model systems, we found that, besides a few tightly bound nucleotides, adjacent solvent-exposed nucleotides interact dynamically with the helicase surfaces. The whole nucleotide segment possessed curved conformations and covered the two RecA-like domains of the helicases, which are essential for the inch-worm mechanism. The synergetic approach reveals that the interactions between the exposed nucleotides and the helicases could be reduced by large stretching forces or electrostatically shielded with high-concentration salt, subsequently resulting in reduced translocation rates of the helicases. The dynamic interactions between the exposed nucleotides and the helicases underlay the force- and salt-dependences of their enzymatic activities. The present single-molecule based approach complements high-resolution structural methods in deciphering the molecular mechanisms of the helicases.

Thermal rearrangement in thermal cascade reaction polymers via ortho-carbonate ester functionalization of polyimides and their gas separation performance

Thermal Rearrangement Polymers have a strong potential regarding to gas separation membrane materials, especially for the purification of methane containing gas streams, due to their inter-connected bottleneck-type pores. However, most TR polymers reveal high permeabilities and high conversions only at high temperatures of 450 °C. Our study demonstrates a method to enhance the permeability at lower annealing temperatures by tailoring the thermally triggered decomposition and reaction cascade via modification and temperature protocol. In this study a set of seven carbonate ester modifications was prepared and thermo-analytically studied by means of DSC and TGA-FTIR on-line analysis combined with structure determination methods and quantum mechanical simulations. Two decarboxylative alkyl-transfer reaction mechanisms were formulated for the decomposition reaction of the carbonate ester group. A correlation of the size and branching of the carbonate ester connected alkyl group and its effect on the film properties and gas separation performance was studied. High polyimide to polybenzoxazole conversions were determined for the modified polymers and a 2008 upper bound performance was obtained for all materials, after annealing at 400 °C. Furthermore, the ethyl carbonate ester (ECO3PI-1) polymer entered the target zone above an ideal selectivity of CO2/CH4 of 30 after aging, making it an attractive membrane material.

PDB‐101: Molecular Explorations through Biology and Medicine

PDB‐101 is an online portal for teachers, students, and the general public to promote exploration of the structural biology of proteins and nucleic acids (pdb101.rcsb.org). Learning about the diverse shapes and functions of these biological macromolecules helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy.Why PDB‐101? Researchers around the world are studying these molecules at the atomic level. These 3D structures are freely available at the Protein Data Bank (PDB), the central storehouse of biomolecular structures. This website builds introductory materials to help beginners get started in the basics of biomolecular structure and function (“101”, as in an entry level course) as well as resources for extended learning.Since 2011, PDB‐101 has been developed by the RCSB PDB, a global resource for the advancement of research and education in biology and medicine. Along with our Worldwide PDB collaborators, RCSB PDB curates, annotates, and makes publicly available the PDB data deposited by scientists around the globe. The RCSB PDB then provides a window to these data through a rich online resource with powerful searching, reporting, and visualization tools for researchers. This information is then streamlined for students and teachers at PDB‐101.Features include the ongoing Molecule of the Month series, educational materials such as paper models, posters, molecular animations, educational curricula and more. The section “Guide to Understanding PDB Data” is a primer for detailed PDB‐specific information: PDB Data, Visualizing Structures, Reading Coordinate Files, scientific methods for structure determination, and more.PDB‐101 also runs annual Video Challenges for high school students. Participants create short videos that tell molecular stories that connect structural biology and medicine. Previous topics have included HIV/AIDS, diabetes, and antimicrobial resistance. The 2022 challenge will focus on Molecular Mechanisms of Cancer.PDB‐101 activities are evaluated using user surveys, feedback from in‐person activities, and website analytics. In 2020, PDB‐101 hosted >850,000 users and >2.6 million page views.

Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ever-increasing share of published structural data. Recent advances in TEM technology and automation have boosted both the speed of data collection and quality of acquired images while simultaneously decreasing the required level of expertise for obtaining cryo-EM maps at sub-3 Å resolutions. While most of such high-resolution structures have been obtained using state-of-the-art 300 kV cryo-TEM systems, high-resolution structures can be also obtained with 200 kV cryo-TEM systems, especially when equipped with an energy filter. Additionally, automation of microscope alignments and data collection with real-time image quality assessment reduces system complexity and assures optimal microscope settings, resulting in increased yield of high-quality images and overall throughput of data collection. This protocol demonstrates the implementation of recent technological advances and automation features on a 200 kV cryo-transmission electron microscope and shows how to collect data for the reconstruction of 3D maps that are sufficient for de novo atomic model building. We focus on best practices, critical variables, and common issues that must be considered to enable the routine collection of such high-resolution cryo-EM datasets. Particularly the following essential topics are reviewed in detail: i) automation of microscope alignments, ii) selection of suitable areas for data acquisition, iii) optimal optical parameters for high-quality, high-throughput data collection, iv) energy filter tuning for zero-loss imaging, and v) data management and quality assessment. Application of the best practices and improvement of achievable resolution using an energy filter will be demonstrated on the example of apo-ferritin that was reconstructed to 1.6 Å, and Thermoplasma acidophilum 20S proteasome reconstructed to 2.1-Å resolution using a 200 kV TEM equipped with an energy filter and a direct electron detector.