Determination Of Three-dimensional Structure Research Articles

A Graph Neural Network-Based Approach to XANES Data Analysis.

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction.

Spiers Memorial Lecture: NMR crystallography.

Chemical function is directly related to the spatial arrangement of atoms. Consequently, the determination of atomic-level three-dimensional structures has transformed molecular and materials science over the past 60 years. In this context, solid-state NMR has emerged to become the method of choice for atomic-level characterization of complex materials in powder form. In the following we present an overview of current methods for chemical shift driven NMR crystallography, illustrated with applications to complex materials.

Beyond Dimerization: Harnessing Tetrameric Coiled-Coils for Nanostructure Assembly.

Versatile DNA and polypeptide-based structures have been designed based on complementary modules. However, polypeptides can also form higher oligomeric states. We investigated the introduction of tetrameric modules as a substitute for coiled-coil dimerization units used in previous modular nanostructures. Tetramerizing helical bundles can run in parallel or antiparallel orientation, expanding the number of topological solutions for modular nanostructures. Furthermore, this strategy facilitates the construction of nanostructures from two identical polypeptide chains. Importantly, tetrameric modules substantially stabilized protein nanostructures against air-water interface denaturation, enabling the determination of the first cryo-electron microscopy three-dimensional structure of a coiled-coil-based nanostructure, confirming the designed agreement of the modules forming a tetrahedral cage.

Biophysical Characterization of a Novel Phosphopentomutase from the Hyperthermophilic Archaeon Thermococcus kodakarensis.

Phosphopentomutases catalyze the isomerization of ribose 1-phosphate and ribose 5-phosphate. Thermococcus kodakarensis, a hyperthermophilic archaeon, harbors a novel enzyme (PPMTk) that exhibits high homology with phosphohexomutases but has no significant phosphohexomutase activity. Instead, PPMTk catalyzes the interconversion of ribose 1-phosphate and ribose 5-phosphate. Here, we report biophysical analysis, crystallization, and three-dimensional structure determination of PPMTk by X-ray diffraction at 2.39 Å resolution. The solved structure revealed a novel catalytic motif, unique to PPMTk, which makes this enzyme distinct from the homologous counterparts. We postulate that this novel catalytic motif may enable PPMTk to isomerize phosphopentose instead of phosphohexose. To the best of our knowledge, this is the first biophysical and structural analysis of a phosphopentomutase from hyperthermophilic archaea.

Enhancing solution structural analysis of large molecular proteins through optimal stereo array isotope labeling of aromatic amino acids

The observation of side-chain peaks of aromatic amino acids is the prerequisite for a high-resolution three-dimensional structure determination of proteins by NMR. However, it becomes difficult with increasing molecular size due to an increased transverse relaxation and the control of the relaxation pathway is needed to achieve the observation. We demonstrated that even for the large molecular size of 82 kDa Malate synthase G (MSG), the aromatic 13C-1H (CH) peaks of Tryptophan (Trp) and Phenylalanine (Phe) residues can be observed with high quality using a systematic stable isotope labeling scheme, Stereo-Array Isotope Labeling (SAIL) method. However, the sequence specific assignments of these peaks relied on the use of amino acid substitutions, employing an inefficient method that required many isotopes labeled samples. In this study, we developed novel SAIL amino acids that allow for the observation of the aromatic ring δ,ζ and the aliphatic β position peak of Phe residues. The application of TROSY-based experiment to the isolated CH moieties resulted in the successful observation of discernible and resolved CH peaks in Phe residues in MSG. In MSG, the sequence-specific assignments of the backbone and Cβ positions have already been confirmed. Therefore, using this labeling method, the δ and β position peaks of Phe residues can be clearly assigned in a sequence-specific and stereospecific manner through experiments based on intra-residue NOE. Furthermore, the NOESY experiment also allows for the acquisition of information pertaining to the conformation of Phe residues, such as the χ1 dihedral angle, providing valuable insights for the determination of accurate protein structures and in dynamic analysis. This new SAIL amino acids open an avenue to achieve a variety of NMR analysis of large molecular proteins, including a high-resolution structure determination and dynamics and interaction analysis.

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.

Breaking Cycles: Saponification-Enhanced NMR Fingerprint Matching for the Identification and Stereochemical Evaluation of Cyclic Lipodepsipeptides from Natural Sources.

We previously described NMR based fingerprint matching with peptide backbone resonances as a fast and reliable structural dereplication approach for Pseudomonas cyclic lipodepsipeptides (CLiPs). In combination with total synthesis of a small library of configurational CLiP congeners this also allows unambiguous determination of stereochemistry, facilitating structure-activity relationship studies and enabling three-dimensional structure determination. However, the on-resin macrocycle formation in the synthetic workflow brings considerable burden and limits universal applicability. This drawback is here removed altogether by also transforming the native CLiP into a linearized analogue by controlled saponification of the ester bond. This eliminates the need for macrocycle formation, limiting the synthesis effort to linear peptide analogues. NMR fingerprints of such linear peptide analogues display a sufficiently distinctive chemical shift fingerprint to act as effective discriminators. The approach is developed using viscosin group CLiPs and subsequently demonstrated on putisolvin, leading to a structural revision, and tanniamide from Pseudomonas ekonensis COR58, a newly isolated lipododecapeptide that defines a new group characterized by a ten-residue large macrocycle, the largest to date in the Pseudomonas CLiP portfolio. These examples demonstrate the effectiveness of the saponification- enhanced approach that broadens applicability of NMR fingerprint matching for the determination of the stereochemistry of CLiPs.

Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.

The utilization of In-silico subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by in vitro and in vivo testing, thereby establishing a connection between the computational predictions and real applications.

The knowns and unknowns of callose biosynthesis in terrestrial plants

Callose, a linear (1,3)-β-glucan, is an indispensable carbohydrate polymer required for plant growth and development. Advances in biochemical, genetic, and genomic tools, along with specific antibodies, have significantly enhanced our understanding of callose biosynthesis. As additional components of the callose synthase machinery emerge, the elucidation of molecular biosynthetic mechanisms is expected to follow. Short-term objectives involve defining the stoichiometry and turnover rates of callose synthase subunits. Long-term goals include generating recombinant callose synthases to elucidate their biochemical properties and molecular mechanisms, potentially culminating in the determination of callose synthase three-dimensional structure. This review delves into the structures and intricate molecular processes underlying callose biosynthesis, emphasizing regulatory elements and assembly mechanisms.

Flexible structural arrangement and DNA-binding properties of protein p6 from Bacillus subtillis phage φ29.

The genome-organizing protein p6 of Bacillus subtilis bacteriophage φ29 plays an essential role in viral development by activating the initiation of DNA replication and participating in the early-to-late transcriptional switch. These activities require the formation of a nucleoprotein complex in which the DNA adopts a right-handed superhelix wrapping around a multimeric p6 scaffold, restraining positive supercoiling and compacting the viral genome. Due to the absence of homologous structures, prior attempts to unveil p6's structural architecture failed. Here, we employed AlphaFold2 to engineer rational p6 constructs yielding crystals for three-dimensional structure determination. Our findings reveal a novel fold adopted by p6 that sheds light on its self-association mechanism and its interaction with DNA. By means of protein-DNA docking and molecular dynamic simulations, we have generated a comprehensive structural model for the nucleoprotein complex that consistently aligns with its established biochemical and thermodynamic parameters. Besides, through analytical ultracentrifugation, we have confirmed the hydrodynamic properties of the nucleocomplex, further validating in solution our proposed model. Importantly, the disclosed structure not only provides a highly accurate explanation for previously experimental data accumulated over decades, but also enhances our holistic understanding of the structural and functional attributes of protein p6 during φ29 infection.

Characterization and structural study of a novel β-N-acetylgalactosaminidase from Niabella aurantiaca.

We report here the identification, characterization and three-dimensional (3D) structure determination of NaNga, a newly identified β-N-acetylgalactosaminidase from the Gram-negative soil bacterium Niabella aurantiaca DSM 17617. Whenrecombinantly expressed in Escherichia coli, the enzyme selectively cleaved 4-nitrophenyl-N-acetyl-β-d-galactosamine (pNP-β-d-GalpNAc). The X-ray crystal structure of the protein was refined to 2.5 Å and consists of an N-terminal β-sandwich domain and a (β/α)8 barrel catalytic domain. Despite a mere 22% sequence identity, the 3D structure of NaNga is similar to those previously determined for family GH123 members, suggesting it also employs the same substrate-assisted catalytic mechanism. Inhibition by N-acetyl-galactosamine thiazoline (GalNAc-thiazoline) supports the suggested mechanism. A phylogenetic analysis of its proximal sequence space shows significant clustering ofunknown sequences around NaNga with sufficient divergence with previously identified GH123 members to subdivide this family into distinctsubfamilies. Although the actual biological substrate of our enzyme remains unknown, examination of the active site pocket suggests that it may bea β-N-acetylgalactosaminide substituted by a monosaccharide at O-3. Analysis of the genomic context suggests, in turn, that this substituted β-N-acetylgalactosaminide may be appended to a d-arabinan from an environmental Actinomycete.

Determining Protein Structures Using X-Ray Crystallography.

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.

Three-dimensional structure determination via correlated scattering with an X-ray laser

Three-dimensional structure determination <i>via</i> correlated scattering with an X-ray laser

Advances in Peptide/Protein Structure Prediction Tools and their Relevance for Structural Biology in the Last Decade

Abstract: Peptides and proteins are involved in several biological processes at a molecular level. In this context, three-dimensional structure characterization and determination of peptides and proteins have helped researchers unravel the chemical and biological role of these macromolecules. Over 50 years, peptide and protein structures have been determined by experimental methods, including nuclear magnetic resonance (NMR), X-ray crystallography, and cryo-electron microscopy (cryo-EM). Therefore, an increasing number of atomic coordinates for peptides and proteins have been deposited in public databases, thus assisting the development of computational tools for predicting unknown 3D structures. In the last decade, a race for innovative methods has arisen in computational sciences, including more complex biological activity and structure prediction algorithms. As a result, peptide/protein theoretical models have achieved a new level of structure prediction accuracy compared with experimentally determined structures. Machine learning and deep learning approaches, for instance, incorporate fundamental aspects of peptide/protein geometry and include physical/biological knowledge about these macromolecules' experimental structures to build more precise computational models. Additionally, computational strategies have helped structural biology, including comparative, threading, and ab initio modeling and, more recently, prediction tools based on machine learning and deep learning. Bearing this in mind, here we provide a retrospective of protein and peptide structure prediction tools, highlighting their advances and obstacles and how they have assisted researchers in answering crucial biological questions.

High-resolution three-dimensional structural determination of unstained double-gyroid block copolymers through scanning transmission electron microscopy

Block copolymer-based multicomponent materials have garnered considerable attention because of tunable properties due to their various constituents. The use of electron tomography through transmission electron microscopy (TEM) for the three-dimensional (3D) imaging of stained block copolymers is an established approach for investigating structure-property relationships. Recently, scanning transmission electron microscopy (STEM) with an annular dark-field (ADF) detector has emerged as a method for the 3D structural analysis of unstained block copolymers. However, because of a lack of electron contrast, only a few low-resolution 3D reconstructions were reported for light elements. Herein, we report the first 3D structural analysis of a 200-nm-thick film composed of unstained double-gyroid block copolymers-polystyrene-b-poly(2-vinylpyridine) (PS-P2VP)-at a resolution of 8.6 nm through spherical aberration Cs-corrected STEM. At this resolution, P2VP molecules can be distinguished from PS molecules in z-contrast 3D reconstructions obtained both experimentally and theoretically. The 3D reconstructions revealed structural differences between stained and unstained specimens.

Resolution of Virtual Depth Sectioning from Four-Dimensional Scanning Transmission Electron Microscopy.

One approach to three-dimensional structure determination using the wealth of scattering data in four-dimensional (4D) scanning transmission electron microscopy (STEM) is the parallax method proposed by Ophus et al. (2019. Advanced phase reconstruction methods enabled by 4D scanning transmission electron microscopy, Microsc Microanal25, 10-11), which determines the scattering matrix and uses it to synthesize a virtual depth-sectioning reconstruction of the sample structure. Drawing on an equivalence with a hypothetical confocal imaging mode, we derive contrast transfer and point spread functions for this parallax method applied to weakly scattering objects, showing them identical to earlier depth-sectioning STEM modes when only bright field signal is used, but that improved depth resolution is possible if dark field signal can be used. Through a simulation-based study of doped Si, we show that this depth resolution is preserved for thicker samples, explore the impact of shot noise on the parallax reconstructions, discuss challenges to making use of dark field signal, and identify cases where the interpretation of the parallax reconstruction breaks down.

In silico detection and characterization of novel virulence proteins of the emerging poultry pathogen Gallibacterium anatis.

The pathogen Gallibacterium anatis has caused heavy economic losses for commercial poultry farms around the world. However, despite its importance, the functions of its hypothetical proteins (HPs) have been poorly characterized. The present study analyzed the functions and structures of HPs obtained from Gallibacterium anatis (NCTC11413) using various bioinformatics tools. Initially, all the functions of HPs were predicted using the VICMpred tool, and the physicochemical properties of the identified virulence proteins were then analyzed using Expasy's ProtParam server. A virulence protein (WP_013745346.1) that can act as a potential drug target was further analyzed for its secondary structure, followed by homology modeling and three-dimensional (3D) structure determination using the Swiss-Model and Phyre2 servers. The quality assessment and validation of the 3D model were conducted using ERRAT, Verify3D, and PROCHECK programs. The functional and phylogenetic analysis was conducted using ProFunc, STRING, KEGG servers, and MEGA software. The bioinformatics analysis revealed 201 HPs related to cellular processes (n = 119), metabolism (n = 61), virulence (n = 11), and information/storage molecules (n = 10). Among the virulence proteins, three were detected as drug targets and six as vaccine targets. The characterized virulence protein WP_013745346.1 is proven to be stable, a drug target, and an enzyme related to the citrate cycle in the present pathogen. This enzyme was also found to facilitate other metabolic pathways, the biosynthesis of secondary metabolites, and the biosynthesis of amino acids.

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments.

Members of the Bacillus genus are industrial cell factories due to their capacity to secrete significant quantities of biomolecules with industrial applications. The Bacillus paralicheniformis strain Bac84 was isolated from the Red Sea and it shares a close evolutionary relationship with Bacillus licheniformis. However, a significant number of proteins in its genome are annotated as functionally uncharacterized hypothetical proteins. Investigating these proteins' functions may help us better understand how bacteria survive extreme environmental conditions and to find novel targets for biotechnological applications. Therefore, the purpose of our research was to functionally annotate the hypothetical proteins from the genome of B. paralicheniformis strain Bac84. We employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation, physicochemical characterization, subcellular localization, protein-protein interactions, and three-dimensional structure determination. Sequences of 414 hypothetical proteins were evaluated and we were able to successfully attribute a function to 37 hypothetical proteins. Moreover, we performed receiver operating characteristic analysis to assess the performance of various tools used in this present study. We identified 12 proteins having significant adaptational roles to unfavorable environments such as sporulation, formation of biofilm, motility, regulation of transcription, etc. Additionally, 8 proteins were predicted with biotechnological potentials such as coenzyme A biosynthesis, phenylalanine biosynthesis, rare-sugars biosynthesis, antibiotic biosynthesis, bioremediation, and others. Evaluation of the performance of the tools showed an accuracy of 98% which represented the rationality of the tools used. This work shows that this annotation strategy will make the functional characterization of unknown proteins easier and can find the target for further investigation. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis strain Bac84 in effectively creating a new biotechnological target. In addition, the results may also facilitate a better understanding of the survival mechanisms in harsh environmental conditions.

CCP4 Cloud for structure determination and project management in macromolecular crystallography.

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4.

Three-dimensional structure determination of gold nanotriangles in solution using X-ray free-electron laser single-particle analysis

X-ray free-electron lasers (XFELs) have allowed the imaging of nanoscale samples in near-physiological conditions. To achieve three-dimensional (3D) nanostructural reconstruction, many challenges need to be addressed, such as sample delivery for data collection and data processing of noisy diffraction patterns. Here, we provided a demonstration of the 3D structure reconstruction of a gold nanoparticle from XFEL diffraction data measured at the SPring-8 Angstrom Compact Free-Electron Laser using microliquid enclosure arrays (MLEAs) sample holders. MLEAs enable in-solution measurements, however, they induce a significant amount of background noise. Thus, we performed a series of data analyses to identify the diffraction patterns suitable for 3D reconstruction as well as nonhit patterns to estimate the background noise. The background subtraction from the data significantly improved the quality of the restored structure, with the resolution estimated to be 5 nm using Fourier shell correlation. Our paper has revealed the notable potential of XFEL imaging using MLEAs in combination with the developed data-analysis protocol.