Structure Determination Research Articles

CryoSeek: A strategy for bioentity discovery using cryoelectron microscopy

Structural biology is experiencing a paradigm shift from targeted structural determination to structure-guided discovery of previously uncharacterized bioentities. We employed cryoelectron microscopy (cryo-EM) to analyze filtered water samples collected from the Tsinghua Lotus Pond. Here, we report the structural determination and characterization of two highly similar helical fibrils, named TLP-1a and TLP-1b, each approximately 8 nm in diameter with a 15-Å wide tunnel. These fibrils are assembled from a similar protein protomer, whose structure was conveniently automodeled in CryoNet. The protomer structure does not match any available experimental structures, but shares the same fold as many predicted structures of unknown functions. The amino-terminal β strand of protomer n + 4 inserts into a cleft in protomer n to complete an immunoglobulin (Ig)-like domain. This packing mechanism, known as donor-strand exchange (DSE), has been observed in several bacterial pilus assemblies, wherein the donor is protomer n + 1. Despite distinct shape and thickness, this reminiscence suggests that TLP-1a/b fibrils may represent uncharacterized bacterial pili. Our study demonstrates an emerging paradigm in structural biology, where high-resolution structural determination precedes and drives the identification and characterization of completely unknown objects.

Fractal analyses of AlxGa1−xN thin film surfaces on AlN at different annealing temperatures

Abstract The determination of heteromorphological structures formed on the surface during annealing of AlxGa1-xN thin film grown on sapphire substrate using the metal organic chemical vapor deposition technique at different temperatures was investigated by fractal analysis method. The images of the surfaces of the thin films were taken by atomic force microscopy (AFM) at temperatures of 900, 1000, 1050 and 1200 °C respectively. AFM images were digitised in bitmap format according to the annealing temperatures. It was determined that the fractal dimensions obtained a linear correlation with the annealing temperatures. The results confirm the hypothesis that surface relaxation by the thermal action can produce fractal-like structures at particle or cluster boundaries. It is found that the observed cluster formation of superficial particles decreases as increasing temperature. The increase in temperature reduces the rate of superficial particle coating. To determine the surface roughness of the AlxGa1-xN thin film according to the annealing temperature, the AFM images were digitized in tagged image file format and the statistical root mean square, mean value, mean roughness, skewness and kurtosis values of the films were calculated. The roughness is a result of the thin film surface heteromorphology formed due to the specific annealing process. It is proved that the fractal dimensions of the AlxGa1-xN thin film increase as the annealing temperature rises. The particles coalesce on the surface and cluster in different types and sizes at each different annealing temperature, forming islets of different sizes. The skewness and kurtosis values show a different and irregular change.

Structural Study on Alkali‐Carboxylate Functionalized Late‐Transition Metal Bis(dithiocarbamate) Complexes

AbstractLithium, sodium, and potassium derivatives of amino‐acid derived bis(dithiocarbamate) complexes of bivalent nickel, palladium, platinum, and copper are readily available by treatment of the carboxylic‐acid functionalized complexes with the respective alkali metal hydroxide. Representative derivatives of the types A4[M(L1)2], A2[M(HL1)2], A[M(HL1)(H2L1)] (N‐dithioato‐iminodiacetate, L1), and A2[M(L2,3)2] (N‐dithioato‐l‐prolinate, L2, and ‐N‐benzylglycinate, L3; A=Li, Na, K; M=Ni, Pd, Pt, Cu) were isolated and characterized by spectroscopic and thermal methods. Six entries were accessible to single‐crystal X‐ray structure determination, revealing the presence of different coordination‐polymeric structures and hydrogen‐bonded assemblies comprising solvent‐separated cations in the solid state. While the transition metal always retains its all‐sulfur coordination with defined geometry, the alkali metals are structurally more flexible. The latter are usually coordinated by carboxylate and water, but additional interactions with sulfur donors are also relevant in some cases.

Superlattice Assembly for Empowering Metal Nanoclusters.

ConspectusAtomically precise metal nanoclusters, serving as an aggregation state of metal atoms, display unique physicochemical properties owing to their ultrasmall sizes with discrete electronic energy levels and strong quantum size effects. Such intriguing properties endow nanoclusters with potential utilization as efficient nanomaterials in catalysis, electron transfer, drug delivery, photothermal conversion, optical control, etc. With the assistance of atomically precise operations and theoretical calculations on metal nanoclusters, significant progress has been accomplished in illustrating their structure-performance correlations at the single-molecule level. Such research achievements, in turn, have contributed to the rational design and customization of functional nanoclusters and cluster-based nanomaterials.Most previous studies have focused on investigating structure-property correlations of nanocluster monomers, while the exploration of electronic structures and physicochemical properties of hierarchical cluster-based assembled structures was far from enough. Indeed, from the application aspect, the nanoclusters with controllably assembly states (e.g., crystalline assembled materials, host-guest hybrid materials, amorphous powders, and so on) were more suitable for performance expression relative to those in the monomeric state and more directed to downstream solid-state applications. In this context, more attention should be paid to the state-correlated property variations of metal nanoclusters occurring in their aggregating and assembling processes for better applications in accordance with their aptitude.Crystalline aggregates are crucial in the structural determination of metal nanoclusters, also acting as a cornerstone to analyze the structure-property correlations by affording atomic-level information. The regular arrangement, uniform composition, and close intermolecular distance of the cluster molecules in their supercrystal lattices are beneficial for property retention and amplification from the molecule itself as a monomeric state. Besides, for these nanoparticles with strong quantum size effects, the intercluster distances in the supercrystal lattices are still located at the nanoscale level, wherein the quantum size effect is highly likely to take effect with additional intermolecular synergistic effects. Accordingly, it is expected that novel performances might occur in the crystalline aggregates of nanoclusters that are completely different from those in the monomolecular state.In this Account, we emphasize our efforts in exploring the performance enhancement of atomically precise metal nanoclusters in their crystalline aggregate states, such as thermal stability, photoluminescence, optical activity, and an optical waveguide. Such performance enhancements further supported the practical uses of metal nanoclusters in structure determination, a polarization switch, an optical waveguide device, and so on. We also demonstrated that the differences in physicochemical properties between crystalline aggregates and monomers of metal nanoclusters might be attributed to the change in electronic structures during the crystalline aggregation processes in the superlattice. The "superlattice assembly" is intended to customize the function of cluster-based aggregates for downstream solid-state applications.

Study of the Torquoselectivity of a Set of Unusual Ring‐Opening Electrocyclic Reactions: Determination of the Electronic Bonding Structure Through the Methodologies of Natural Bond Orbital Analysis and Quantum Theory of Atoms in Molecules, and Analysis of the Electronic Reaction Mechanism Through Bond Reactivity Descriptors

ABSTRACTIn this work, we have studied the torquoselectivity of a set of unusual ring‐opening electrocyclic reactions that have not been successfully rationalized using models based on orbital symmetry nor have they been explained by steric hindrance. Firstly, the corresponding transition states have been obtained, and it has been verified that the intrinsic reaction coordinates associated with these transition states are consistent with the reactants and products of the reactions studied. This has allowed us to theoretically calculate the reaction barriers (with their corresponding thermal corrections) and compare them with the corresponding experimental values. In a second step, we have analyzed the electronic bonding structure using the methodologies of Natural Bond Orbital Analysis and Quantum Theory of Atoms in Molecules, searching for interactions that may significantly stabilize the transition states. Finally, we have analyzed the reactivity using a recent model that has allowed us to calculate the corresponding bond reactivity descriptors.

The Impact of Employee Stock Ownership Plans on Capital Structure Decisions: Evidence from China

The determination of the capital structure is a critical component of a company’s financial decision-making process. The question of how to optimize a firm’s capital structure to increase its value has been a significant topic of interest within the financial community. The employee stock ownership plan (ESOP) has developed rapidly in China’s capital market over the past decade, providing a suitable context for studying the impact of employee equity incentives on capital structure decisions. This paper employs cross-sectional ordinary least squares regression models and unbalanced panel fixed effect models to investigate the impact of employee stock ownership plans (ESOPs) on firms’ capital structure decisions. The analysis is conducted on a sample of Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges. The research considers both static capital structure choice and dynamic capital structure adjustment. We find that the implementation of an ESOP reduces the level of corporate debt and accelerates the dynamic adjustment of capital structure, suggesting that employee equity incentives play a role in optimizing firms’ capital structure decisions. We also find that the impact of ESOPs on the dynamic adjustment of capital structure is asymmetric. Specifically, the implementation of ESOPs markedly accelerates the downward adjustment of capital structure, yet has no impact on the upward adjustment of capital structure. Further analysis demonstrates that the impact of ESOPs on capital structure decisions is contingent upon the macroeconomic environment, industry characteristics, corporate governance, and ESOP contract designs. First, the optimization of ESOPs on capital structure decisions is more pronounced in an economic boom environment, in a poor market climate, or in competitive industries. Second, the reduction effect of ESOPs on corporate debt is more pronounced in non-state-owned companies, high-tech companies and those with lower ownership concentration. In contrast, the acceleration effect of ESOPs on capital structure adjustment is more pronounced in state-owned companies, non-high-tech companies and those with higher ownership concentration. Ultimately, ESOPs financed by loans from a firm’s major shareholders—or with a longer lock-up period, smaller shareholding size or executive subscription ratio—demonstrate a more pronounced optimization effect on capital structure decisions. This paper not only contributes to the existing literature on the relationship between equity incentives and capital structure decisions, but also provides guidance for listed companies on the reasonable design of their ESOPs and the optimization of their capital structure decisions.

Four decades of Eukaryotic DNA replication: From yeast genetics to high-resolution cryo-EM structures of the replisome

I had my eyes set on DNA replication research when I took my first molecular biology course in graduate school. My election to the National Academy of Sciences came just when I was retiring from active research. It gives me an opportunity to reflect on my personal journey in eukaryotic DNA replication research, which started as a thought experiment and culminated in witnessing the determination of the cryoelectron microscopic structure of the yeast replisome in the act of transferring histone-encoded epigenetic information at the replication fork. I would like to dedicate this inaugural article to my talented trainees and valuable collaborators in gratitude for the joy they gave me in this journey. I also want to thank my mentors who instilled in me the purpose of science. I hope junior scientists will not be disheartened by the marathon nature of research, but mindful enough to integrate and pause for other equally fun and meaningful activities of life into the marathon.

The role of structure in regulatory RNA elements.

Regulatory RNA elements fulfill functions such as translational regulation, control of transcript levels, and regulation of viral genome replication. Trans-acting factors (i.e. RNA-binding proteins) bind the so-called cis elements and confer functionality to the complex. The specificity during protein-RNA complex (RNP) formation often exploits the structural plasticity of RNA. Functional integrity of cis-trans pairs depends on the availability of properly folded RNA elements, and RNA conformational transitions can cause diseases. Knowledge of RNA structure and the conformational space is needed for understanding complex formation and deducing functional effects. However, structure determination of RNAs under in vivo conditions remains challenging. This review provides an overview of structured eukaryotic and viral RNA cis elements and discusses the effect of RNA structural equilibria on RNP formation. We showcase implications of RNA structural changes for diseases, outline strategies for RNA structure-based drug targeting, and summarize the methodological toolbox for deciphering RNA structures.

Alkylaluminum Complexes Featuring Bridged Bis-Formylfluorenimide Ligands for Hydroboration of Aldehyde, Ketone, and Imines.

Three bis-formylfluorenimide ligands with different bridging groups were designed and synthesized, leading to the successful preparation of six novel alkylaluminum complexes through their reaction with alkylaluminum reagents (AlMe3 or AlEt3). Complexes 1 and 2 were obtained by the reaction of 1,2-propylene-bridged diamine (L1) with AlMe3 or AlEt3. By reacting 1,2-cyclohexylene-bridged diamine (L2) with AlMe3 or AlEt3 to obtain complexes 3 and 4. The above ligands formed a bidentate four-coordinate structure with alkylaluminum, which involved the elimination of one alkyl group as the ligand reacted with alkylaluminum. The complexes 5 and 6 were synthesized through the reaction of 1,2-phenylene-bridged diamine (L3) with AlEt3 in toluene or tetrahydrofuran. Notably, L3 exhibited unique reactivity compared with the other ligands, which formed a tridentate four-coordinated structure when reacting with alkylaluminum. The formation of the tridentate complex resulted from the introduction of a benzimidazole derivative or tetrahydrofuran (THF) molecule along with the elimination of two alkyl groups during its coordination with alkylaluminum. All complexes were characterized via 1H NMR, 13C NMR, and elemental analysis, with structural determination confirmed through X-ray. Furthermore, the catalytic activity in the hydroboration reaction of aldehyde, ketone, and imines was investigated with these complexes as catalysts. Among them, complex 1 demonstrated excellent catalytic performance (up to 99% yield) and broad substrate compatibility (more than 30 substrates) at low catalyst loading (1 mol %) under mild reaction conditions.

Advances and Challenges in Scoring Functions for RNA–Protein Complex Structure Prediction

RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA–protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA–protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA–protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA–protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA–protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field.

Rapid design method for composite emergency steel piers with thin-walled steel tube framework and discussion on "temporary-permanent" transition

To address the conflict between the reserve of repair materials for existing bridge piers and the demands for emergency repairs, as well as the challenge of converting temporary emergency structures into permanent ones without significant disassembly, a novel composite emergency steel pier has been developed. This pier utilizes commonly available thin-walled steel tube sections as the framework, I-beams as the connecting elements, and incorporates a "temporary-permanent" transition function. Taking the emergency steel pier for medium-height bridge piers in high-speed rail systems as an example, this study introduces three evaluation metrics—security coefficient (n), stability coefficient k), and variation coefficient (V)—analyzing the factors influencing the design and application thresholds of the emergency steel piers from perspectives of strength, stability, and load distribution uniformity. Based on this, a rapid design method for composite emergency steel piers with thin-walled steel tube frameworks that does not require numerical simulation has been developed and experimentally validated. Research findings indicate that when used for the emergency repair of medium-height or lower bridge piers in high-speed railways, the column line stiffness of the thin-walled steel tube framework composite emergency steel piers should range between 0.34 × 10 exp7 kN/mm to 4.15 × 10 exp7 kN/mm. The transverse and longitudinal spacing of the columns should be 1 m to 3 m and 1.5 m to 3 m respectively. The line stiffness of the plinth beams I and II should be greater than 0.69 kN/mm and 1.01 kN/mm respectively. Within these parameter ranges, the emergency steel pier will not experience strength failure or instability, and the load distribution among the columns remains uniformly favorable. In applications to other scenarios, the rapid generation of composite emergency steel piers with thin-walled steel tube frameworks can be achieved by following three steps: parameter selection and structural configuration determination, calculation of the pier-beam linear stiffness ratio, and criterion-based result verification. The rapid design and implementation of composite emergency steel piers help alleviate the contradiction between material stockpiling and repair demands. Furthermore, the pre-designed "temporary-permanent" transition functionality facilitates integrated operations for emergency repairs and permanent restorations.

Unveiling Short-Range Magnetic Correlations: The Development of Magnetic Pair Distribution Function Method at CSNS

Recent advancements in the magnetic pair distribution function (mPDF) analysis of neutron total scattering data provide a powerful approach to investigate local magnetic correlations in materials. This technique is promising for revealing short-range magnetic correlations at the sub-nanometer length scale directly in real space. It is particularly suitable for strongly correlated electron systems and geometrically frustrated magnets. In this study, the mPDF experiment is conducted at the multi-physics instrument (MPI) of the China Spallation Neutron Source, one of the latest neutron total scattering diffractometers in the world. We systematically benchmarked a series of important parameters related to the experimental setup and data processing for mPDF experiments at the MPI and similar instruments. This method not only advances the magnetic structure determination of fundamental materials but also opens the door for extending mPDF studies to more complicated and frontier magnetic systems that are challenging for conventional diffraction methods.

Structural determination of a carbocation-derived rearrangement product observed during Thiele cage opening: insight into the mechanism of an alkyl-extended pinacol reaction

A substituted bishomocubane structure colloquially known as a Thiele cage was previously observed to undergo an alkyl-extended pinacol-type rearrangement, wherein 1,2-aryl migration and ketone formation occurred together with (or at least in close succession to) opening of a strained cyclobutane bond. While there was some indication that the rearrangement reaction might proceed via a stepwise process involving a sequence of carbocation intermediates, previous studies did not uncover any direct evidence for the formation of carbocations, and did not fully explain the regio- and stereospecificity of the reaction. Here we describe the isolation and detailed characterization of a second rearrangement product formed under the pinacol reaction conditions, the existence of which implicates the formation of discrete carbocation intermediates along the reaction pathway. Observation of this new product also finally explains the fate of any Thiele cage material that is converted to a carbocation, but which is not geometrically predisposed to react through a facile 1,2-aryl migration. As such, our findings resolve previous questions surrounding the origin of regio- and stereospecificity in the alkyl-extended pinacol rearrangement.

DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

Understanding Barriers to STEM: Teachers' Insights on African American Underrepresentation

This study focuses on the development of a measurement instrument to identify Middle and High School mathematics and science teachers’ beliefs about the main factors of underrepresentation of African American students in STEM (Science, Technology, Engineering, and Mathematics) fields. The research method using the stages of instrument development were (1) test design which involved construction of initial item pool by conducting extensive review of literature and coding of participating teachers personal statements (2) determination of validity which involved expert review process to confirm construct and face validity of items, (3) pilot testing which involved collecting data from a second set of participants, (4) determination of reliability which involves conducting a reliability analysis based on data collected from pilot testing and (5) determination of factorial structure which involved Exploratory Factor Analysis (EFA) to identify underlying factors in participating teachers’ belief structures. First, to obtain trustworthy information, voluntary middle and high school mathematics and science teachers were asked to write personal statements where they explain their main reasons for choosing teaching as a career. Secondly, qualitative data obtained from participating teachers’ personal statements were coded. Coding was applied to identify and to group the phrases or sentences that convey the same or similar reasons. Then results from an extensive literature review on this topic were blended in teachers’ common expressions which were determined because of coding procedure. Then 8 content experts assessed content validity and face validity. Finally, the survey was piloted to African American middle and high school mathematics and science teachers. Fifty-three (53) surveys were completed and received back from teachers. Then, exploratory factor analysis was conducted to identify any underlying factors in the scale. Reliability analyses were also conducted for both the entire survey and for each of the factors. Results of the study suggested four underlying components: (1) Access and Resources, (2) Role Models and Representation, (3) Bias and Discrimination, (4) Curriculum and Pedagogy.

Homonuclear Super‐Resolution NMR Spectroscopy

Homonuclear 1H NMR (nuclear magnetic resonance) spectra such as the [1H,1H]‐NOESY (Nuclear Overhauser Enhancement spectroscopy) are essential for biomolecular structural biology. However, the spectrum contains hundreds to thousands of cross peaks meaning that within approximately 100 ppm2 there is significant signal overlap. Spectral resolution is thus a limiting factor for data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths' of cross peaks in [1H,1H]‐NOESY and [1H,1H]‐TOCSY spectra can be further reduced by a factor of 2–3 in each dimension by super‐resolution spectroscopy. In the indirect dimension a composite exponential‐cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function. Furthermore, measurement time saving by reduced‐acquisition super‐resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

Homonuclear Super-Resolution NMR Spectroscopy.

Homonuclear 1H NMR (nuclear magnetic resonance) spectra such as the [1H,1H]-NOESY (Nuclear Overhauser Enhancement spectroscopy) are essential for biomolecular structural biology. However, the spectrum contains hundreds to thousands of cross peaks meaning that within approximately 100 ppm2 there is significant signal overlap. Spectral resolution is thus a limiting factor for data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths' of cross peaks in [1H,1H]-NOESY and [1H,1H]-TOCSY spectra can be further reduced by a factor of 2-3 in each dimension by super-resolution spectroscopy. In the indirect dimension a composite exponential-cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function. Furthermore, measurement time saving by reduced-acquisition super-resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

Influence of the extraction method on functional properties of commercial snail secretion filtrates

Snail secretion is a complex mixture of several components, including proteins, glycoproteins, mucopolysaccharides and smaller molecules. Its growing use in nutraceutical, cosmetic and biomedical applications, as well as a component of edible and green packaging to replace chemical plasticizer, implies more affordable and sustainable extraction methods. We chose four extracts obtained from Cornu aspersum snails, different by origin, extraction medium (namely, citric acid, lactic acid or none) and additives and we performed a series of characterizations including the SDS-page, the measure of pH and density, the evaluation of dry matter and of protein content, supported by structural determinations by means of UV-visible and infrared spectroscopy, X-Rays diffraction and thermogravimetric measurements. Biological assays comprising evaluation of cytotoxicity and antibacterial activity were also carried out. All the tests were performed both on the as received snail filtrates and on the samples after proper dialysis to remove preservatives added by manufacturers. The obtained results put into evidence that the properties and composition of the final extract are strongly influenced by the collection method, that can be relevant for the proper use of snail filtrate in specific applications.

Bioinformatic study of keratinases from the S01 family from Streptomyces microflavus strain Dsm 40593

Aim. Determination and study of the primary structures and domain organization of keratinase precursors (serine proteases from the S01 family) of S. microflavus DSM 40593 using bioinformatic methods. Methods. In the research information presented in databases on the NCBI server was analyzed. Computerized sequence analysis was performed using the BLAST program on the NCBI server. Results. BLAST analysis of the genome sequence of the strain S. microflavus DSM 40593 was carried out using the sequence of the precursor AGK81825 as a query. Thanks to bioinformative analysis, seven genes encoding precursors of serine proteases from the S01 family were found in the genome of the strain S. microflavus DSM 40593. Conclusions. In the genomes of streptomycetes, there may be more than one gene encoding precursors of keratinases from the S01 family. It was established that the amino acid sequences of the enzymatic domains of keratinase precursors from the S01 family are characterized by higher similarity indices than the sequences of foldase domains.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.