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A new polymorph of 1,3-diacetylpyrene has been obtained from its melt and thoroughly characterized using single-crystal X-ray diffraction, steady-state UV-Vis spectroscopy and periodic density functional theory calculations. Experimental studies covered the temperature range from 90 to 390 K and the pressure range from atmospheric to 4.08 GPa. Optimal sample placement in a diamond anvil cell according to our previously presented methodology ensured over 80% data coverage up to 0.8 Å for a monoclinic sample. Unrestrained Hirshfeld atom refinement of the high-pressure crystal structures was successful and anharmonic behavior of carbonyl oxygen atoms was observed. Unlike the previously characterized polymorph, the structure of 2°AP-β is based on infinite π-stacks of antiparallel 2°AP molecules. 2°AP-β displays piezochromism and piezofluorochromism which are directly related to the variation in interplanar distances within the π-stacking. The importance of weak intermolecular interactions is reflected in the substantial negative thermal expansion coefficient of -55.8 (57) MK-1 in the direction of C-H...O interactions.

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide–receptor complexes, along with Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin's interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides. [ABSTRACT FROM AUTHOR]

Bet v 1, the European White Birch tree pollen allergen is responsible for a number of allergic responses in humans such as rhinitis, asthma and oral allergy syndrome. The allergen belongs to pathogenesis-related (PR) class 10 protein superfamily and exists in several naturally occurring isoforms. Limited structural information on Bet v 1 isoallergens and variants prompted us to carry out their in silico structural characterization. In this study, three-dimensional structures of Bet v 1 isoallergens were predicted followed by allergen-antibody docking with Bet v 1- specific human IgE. Further, molecular dynamics simulations were performed for the allergen-antibody complexes. In addition, in silico mutagenesis was carried out for the design of a hypoallergenic variant of Bet v 1. Our study aimed to elucidate the differential ability of Bet v 1 isoallergens in eliciting allergic responses based on structural features and also identified a potential hypoallergen allowing us to propose it as a promising candidate for treating birch pollen-induced allergy. [ABSTRACT FROM AUTHOR]

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools. [ABSTRACT FROM AUTHOR]

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL‐E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology. [ABSTRACT FROM AUTHOR]

Nanobodies, the smallest functional antibody fragment derived from camelid heavy‐chain‐only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen‐binding domains, highlighting the critical role of complementarity‐determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen‐binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody‐based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

AlphaFold and similar groundbreaking, AI‐based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab‐initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI‐based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI‐driven advancements. Indeed, user‐driven intervention is still as pivotal in the structure prediction process as in complementing state‐of‐the‐art algorithms with functional and biological knowledge.

With the support of spatial-temporal data analysis technologies and network science, the International Trade Network (ITN) research has made significant progress, demonstrating broad application prospects in mining market evolution and predicting trade dynamics. Based on all ITN research cases from 2003 to 2023, this paper presents a research framework for ITN analysis, reviewing its advancements in data collection, visualization, topology analysis, structure prediction, and correlation analysis, where the spatial-temporal data analysis technologies have provided crucial methodologies and insights. A multilevel scenario construction theory for complex networks is proposed, highlighting the great significance of spatial regression models and system dynamics models in identifying global mechanisms; the unique value of temporal network analysis under the support of time-series information is discussed. Given the existing limitations, the development of more granular and reliable datasets utilizing big data technologies, as well as the construction of richer spatial-temporal scenarios, are considered as future research agendas.

Abstract AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight “structure prediction” (s = 12.40, R2 = 0.9480, p = 0.0051), “artificial intelligence” (s = 5.00, R2 = 0.8096, p = 0.0375), “drug discovery” (s = 1.90, R2 = 0.7987, p = 0.0409), and “molecular dynamics” (s = 2.40, R2 = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that “structure prediction, artificial intelligence, molecular dynamics” (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), “sars-cov-2, covid-19, vaccine design” (RP = 97.8%, DP = 37.5%), and “homology modeling, virtual screening, membrane protein” (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.

Tantalum carbide (TaC) is an extremely hard, brittle, refractory ceramic material with excellent physical properties, which makes it a desirable material in e. g. aerospace industries. In order to explore the range of feasible modifications of TaC, we have executed a crystal structure prediction study of the TaC chemical system using a multi‐methodological approach, via enthalpy landscape explorations of pristine TaC at different pressures, supplemented by data mining searches in the ICSD database. Local structure relaxations have been accomplished by using Density Functional Theory (DFT). The global minimum is found to correspond to the equilibrium rock salt (NaCl) type modification. Additionally, eight new phases of tantalum carbide are predicted to be feasible: the WC‐type, the NiAs‐type, the 5‐5‐type, the ZnS‐type, the Ring_TaC‐type, the CsCl‐type, the Ortho_TaC‐type, and the Tetra_TaC‐type. Furthermore, the elastic and mechanical properties of the predicted TaC modifications were explored on the DFT level of computation. The promising values of some of the mechanical properties of the proposed tantalum carbide modifications suggest that various scientific, industrial, and technological applications of TaC should be possible. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected. [Display omitted] • Ensemble prediction quality depends on training input to AlphaFold 2 (AF2) • MSA subsampling predicts ensembles but may miss key protein conformations • Current ensembles cannot reliably determine free energy, conformations, or properties • ⁠Ensemble data is crucial to improve conformational model accuracy Riccabona et al. underscore the importance of accurate structural data in predicting protein structural ensembles. They note that although rapid methods like MSA subsampling can generate ensembles, they often overlook functionally significant conformations, thereby missing crucial kinetic and thermodynamic insights. [ABSTRACT FROM AUTHOR]

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn. [ABSTRACT FROM AUTHOR]

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions. [ABSTRACT FROM AUTHOR]

AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight "structure prediction" (s = 12.40, R2 = 0.9480, p = 0.0051), "artificial intelligence" (s = 5.00, R2 = 0.8096, p = 0.0375), "drug discovery" (s = 1.90, R2 = 0.7987, p = 0.0409), and "molecular dynamics" (s = 2.40, R2 = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that "structure prediction, artificial intelligence, molecular dynamics" (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), "sars-cov-2, covid-19, vaccine design" (RP = 97.8%, DP = 37.5%), and "homology modeling, virtual screening, membrane protein" (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Many Gram-negative enterobacteria translocate virulence proteins (effectors) into intestinal epithelial cells using a type III secretion system (T3SS) to subvert the activity of various cell functions possess. Many T3SS effectors have been extensively characterized, but there are still some effector proteins whose functional information is completely unknown. Methods: In this study, two predicted effectors of unknown function, EspN and EspS (Escherichia coli secreted protein N and S), were selected for analysis of translocation, distribution and structure prediction. Results: The TEM1 (β-lactamase) translocation assay was performed, which showed that EspN and EspS are translocated into host cells in a T3SS-dependent manner during bacterial infection. A phylogenetic tree analysis revealed that homologs of EspN and EspS are widely distributed in pathogenic bacteria. Multiple sequence alignment revealed that EspN and its homologs share a conserved C-terminal region (673–1133 a.a.). Furthermore, the structure of EspN (673–1133 a.a.) was also predicted and well-defined, which showed that it has three subdomains connected by a loop region. EspS and its homologs share a sequence-conserved C-terminal (146–291 a.a.). The predicted structure of EspS (146–291 a.a.) is composed of a β-sheet consisting of four β-strands and several short helices, which has a TM score of 0.5014 with the structure of the Vibrio cholerae RTX cysteine protease domain (PDBID: 3eeb). Conclusions: These results suggest that EspN and EspS may represent two important classes of T3SS effectors associated with pathogen virulence, and our findings provide important clues to understanding the potential functions of EspN and EspS. [ABSTRACT FROM AUTHOR]

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

High-entropy alloys (HEAs) provide limitless opportunities to enhance material performance while predicting the thermodynamic properties and the structures rapidly within HEAs remain challenging. Herein, a new method (M2FEn) for rapidly predicting the formation enthalpies is provided. In contrast to Miedema's semi-empirical methodology, which solely calculates the mixing enthalpy, the M2FEn is capable of calculating the formation enthalpies of various solid solution structures, and by ordering these enthalpies, one can determine the preferred solid-solution structure for the single-phase alloy. The predicted structures using M2FEn are in line with first-principles calculations. The effectiveness of different extrapolation models for formation enthalpy has been assessed. The M2FEn with the Ouyang extrapolation model and the regular solution model have achieved a prediction accuracy of 95.8% and 95.4%, respectively for single-phase HEAs that were experimentally prepared. 28 common HEA elements were selected as the chemical space, and the formation enthalpies of all binary, ternary, quaternary, quinary and senary alloys, a total of 499149 various alloys were calculated by M2FEn. It is found that the BCC structure ratio increases with the increase of the principal element of the alloy. The M2FEn should potentially accelerate the development of novel, high-performance HEAs, enabling more efficient and cost-effective materials design processes.

X-ray scattering/diffraction tensor tomography techniques are promising methods to acquire the 3D texture information of heterogeneous biological tissues at micrometre resolution. However, the methods suffer from a long overall acquisition time due to multi-dimensional scanning across real and reciprocal space. Here, a new approach is introduced to obtain 3D reciprocal information of each illuminated scanning volume using mathematic modeling, which is equivalent to a physical scanning procedure for collecting the full reciprocal information required for voxel reconstruction. The virtual reciprocal scanning scheme was validated by a simulated 6D wide-angle X-ray diffraction tomography experiment. The theoretical validation of the method represents an important technological advancement for 6D diffraction tensor tomography and a crucial step towards pervasive applications in the characterization of heterogeneous materials.

A new polymorph of 1,3-diacetylpyrene has been obtained from its melt and thoroughly characterized using single-crystal X-ray diffraction, steady-state UV–Vis spectroscopy and periodic density functional theory calculations. Experimental studies covered the temperature range from 90 to 390 K and the pressure range from atmospheric to 4.08 GPa. Optimal sample placement in a diamond anvil cell according to our previously presented methodology ensured over 80% data coverage up to 0.8 Å for a monoclinic sample. Unrestrained Hirshfeld atom refinement of the high-pressure crystal structures was successful and anharmonic behavior of carbonyl oxygen atoms was observed. Unlike the previously characterized polymorph, the structure of 2°AP-β is based on infinite π-stacks of antiparallel 2°AP molecules. 2°AP-β displays piezochromism and piezofluorochromism which are directly related to the variation in interplanar distances within the π-stacking. The importance of weak intermolecular interactions is reflected in the substantial negative thermal expansion coefficient of −55.8 (57) MK−1 in the direction of C—H...O interactions.

This paper was motivated by the articles `Same or different – that is the question' in CrystEngComm (July 2020) and `Change to the definition of a crystal' in the IUCr Newsletter (June 2021). Experimental approaches to crystal comparisons require rigorously defined classifications in crystallography and beyond. Since crystal structures are determined in a rigid form, their strongest equivalence in practice is rigid motion, which is a composition of translations and rotations in 3D space. Conventional representations based on reduced cells and standardizations theoretically distinguish all periodic crystals. However, all cell-based representations are inherently discontinuous under almost any atomic displacement that can arbitrarily scale up a reduced cell. Hence, comparison of millions of known structures in materials databases requires continuous distance metrics.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Due to the high impact of semiconductors with respect to many applications for electronics and energy transformation, the search for new compounds and a deep understanding of the structure–property relationship in such materials has a high priority. Electron-precise Zintl compounds of the composition A3TrPn2 (A = Li − Cs, Tr = Al − In, Pn = P, As) have been reported for 22 possible element combinations and show a large variety of different crystal structures comprising zero-, one-, two- and three-dimensional polyanionic substructures. From Li to Cs, the compounds systematically lower the complexity of the anionic structure. For an insight into possible crystal–structure band–structure relations for all compounds (experimentally known or predicted), their band structures, density of states and crystal orbital Hamilton populations were calculated on a basis of DFT/PBE0 and SVP/TZVP basis sets. All but three (Na3AlP2, Na3GaP2 and Na3AlAs2) compounds show direct or pseudo-direct band gaps. Indirect band gaps seem to be linked to one specific structure type, but only for Al and Ga compounds. Arsenides show smaller band gaps than phosphides due to weaker Tr-As bonds. The bonding situation was confirmed by a Mullikan analysis, and most states close to the Fermi level were assigned to non-bonding orbitals. [ABSTRACT FROM AUTHOR]

In this manuscript, a novel presenilin-2 (PSEN2) mutation, Val226Ala, was found in a 59-year-old Korean patient who exhibited rapid progressive memory dysfunction and hallucinations six months prior to her first visit to the hospital. Her Magnetic Resonance Imaging (MRI) showed brain atrophy, and both amyloid positron emission tomography (PET) and multimer detection system-oligomeric amyloid-beta (Aβ) results were positive. The patient was diagnosed with early onset Alzheimer's disease. The whole-exome analysis revealed a new PSEN2 Val226Ala mutation with heterozygosity in the 5th transmembrane domain of the PSEN2 protein near the lumen region. Analyses of the structural prediction suggested structural changes in the helix, specifically a loss of a hydrogen bond between Val226 and Gln229, which may lead to elevated helix motion. Multiple PSEN2 mutations were reported in PSEN2 transmembrane-5 (TM5), such as Tyr231Cys, Ile235Phe, Ala237Val, Leu238Phe, Leu238Pro, and Met239Thr, highlighting the dynamic importance of the 5th transmembrane domain of PSEN2. Mutations in TM5 may alter the access tunnel of the Aβ substrate in the membrane to the gamma-secretase active site, indicating a possible influence on enzyme function that increases Aβ production. Interestingly, the current patient with the Val226Ala mutation presented with a combination of hallucinations and memory dysfunction. Although the causal mechanisms of hallucinations in AD remain unclear, it is possible that PSEN2 interacts with other disease risk factors, including Notch Receptor 3 (NOTCH3) or Glucosylceramidase Beta-1 (GBA) variants, enhancing the occurrence of hallucinations. In conclusion, the direct or indirect role of PSEN2 Val226Ala in AD onset cannot be ruled out. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of pathogenesis-related (PR) proteins, focusing on PR1, PR5, and PR10, in three plant species: Arabidopsis thaliana (At), Solanum lycopersicum (Sl), and Solanum tuberosum (St). We investigated various physico-chemical properties, including protein length, molecular weight, isoelectric point (pI), hydrophobicity, and structural characteristics, such as RMSD, using state-of-the-art tools like AlphaFold and PyMOL. Our analysis found that the SlPR10-StPR10 protein pair had the highest sequence identity (80.00%), lowest RMSD value (0.307 Å), and a high number of overlapping residues (160) among all other protein pairs, indicating their remarkable similarity. Additionally, we used bioinformatics tools such as Cello, Euk-mPLoc 2.0, and Wolfpsort to predict subcellular localization, with AtPR1, AtPR5, and SlPR5 proteins predicted to be located in the extracellular space in both Arabidopsis and S. lycopersicum, while AtPR10 was predicted to be located in the cytoplasm. This comprehensive analysis, including the use of cutting-edge structural prediction and subcellular localization tools, enhances our understanding of the structural, functional, and localization aspects of PR proteins, shedding light on their roles in plant defense mechanisms across different plant species. [ABSTRACT FROM AUTHOR]

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deeplearning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins. [ABSTRACT FROM AUTHOR]

Oriented synthesis of functional materials is a focus of attention in material science. As one of the most important function materials, infrared nonlinear optical materials with large second harmonic generation effects and broad optical band gap are in urgent need. In this work, directed by the theoretical structure prediction, the first series of non‐centrosymmetric (NCS) alkali‐alkaline earth metal [PS4]‐based thiophosphates LiCaPS4 (Ama2), NaCaPS4 (P21), KCaPS4 (Pna21), RbCaPS4 (Pna21), CsCaPS4 (Pna21) were successfully synthesized. Comprehensive characterizations reveal that ACaPS4 could be regarded as promising IR NLO materials, exhibiting wide band gap (3.77–3.86 eV), moderate birefringence (0.027–0.064 at 1064 nm), high laser‐induced damage threshold (LIDT, ~10×AGS), and suitable phase‐matching second harmonic generation responses (0.4–0.6×AGS). Structure‐properties analyses illustrate that the Ca−S bonds show non‐ignorable covalent feature, and [PS4] together with [CaSn] units play dominant roles to determine the band gap and SHG response. This work indicates that Li‐, Na‐ and K‐ analogs may be promising infrared nonlinear optical material candidates, and this is the first successful case of "prediction to synthesis" involving infrared (IR) nonlinear optical (NLO) crystals in the thiophosphate system and may provide a new avenue to the design and oriented synthesis of high‐performance function materials in the future. [ABSTRACT FROM AUTHOR]

Secreted signaling peptides are central regulators of growth, development, and stress responses, but specific steps in the evolution of these peptides and their receptors are not well understood. Also, the molecular mechanisms of peptide-receptor binding are only known for a few examples, primarily owing to the limited availability of protein structural determination capabilities to few laboratories worldwide. Plants have evolved a multitude of secreted signaling peptides and corresponding transmembrane receptors. Stress-responsive SERINE RICH ENDOGENOUS PEPTIDES (SCOOPs) were recently identified. Bioactive SCOOPs are proteolytically processed by subtilases and are perceived by the leucine-rich repeat receptor kinase MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2 (MIK2) in the model plant Arabidopsis thaliana. How SCOOPs and MIK2 have (co)evolved, and how SCOOPs bind to MIK2 are unknown. Using in silico analysis of 350 plant genomes and subsequent functional testing, we revealed the conservation of MIK2 as SCOOP receptor within the plant order Brassicales. We then leveraged AI-based structural modeling and comparative genomics to identify two conserved putative SCOOP-MIK2 binding pockets across Brassicales MIK2 homologues predicted to interact with the "SxS" motif of otherwise sequence-divergent SCOOPs. Mutagenesis of both predicted binding pockets compromised SCOOP binding to MIK2, SCOOP-induced complex formation between MIK2 and its coreceptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1, and SCOOP-induced reactive oxygen species production, thus, confirming our in silico predictions. Collectively, in addition to revealing the elusive SCOOP-MIK2 binding mechanism, our analytic pipeline combining phylogenomics, AI-based structural predictions, and experimental biochemical and physiological validation provides a blueprint for the elucidation of peptide ligand-receptor perception mechanisms. [ABSTRACT FROM AUTHOR]

A new two-dimensional (2D) non-MXene transition metal carbide, Mo3C2, was found using the USPEX code. Comprehensive first-principles calculations show that the Mo3C2 monolayer exhibits thermal, dynamic, and mechanical stability, which can ensure excellent durability in practical applications. The optimized structures of Lix@(3×3)-Mo3C2 (x = 1–36) and Nax@(3×3)-Mo3C2 (x = 1–32) were identified as prospective anode materials. The metallic Mo3C2 sheet exhibits low diffusion barriers of 0.190 eV for Li and 0.118 eV for Na and low average open circuit voltages of 0.31–0.55 V for Li and 0.18–0.48 V for Na. When adsorbing two layers of adatoms, the theoretical energy capacities are 344 and 306 mA h g−1 for Li and Na, respectively, which are comparable to that of commercial graphite. Moreover, the Mo3C2 substrate can maintain structural integrity during the lithiation or sodiation process at high temperature. Considering these features, our proposed Mo3C2 slab is a potential candidate as an anode material for future Li- and Na-ion batteries. [ABSTRACT FROM AUTHOR]

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS‐CoV‐2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation. [ABSTRACT FROM AUTHOR]

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold. [ABSTRACT FROM AUTHOR]

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Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site‐specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site‐specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site‐specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site‐specific rates across many extant protein structures can lead to correct recovery of site‐rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site‐specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site‐specific rates in protein families. [ABSTRACT FROM AUTHOR]

In silico validation of de novo designed proteins with deep learning (DL)‐based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high‐quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water‐soluble and transmembrane β‐barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high‐quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high‐quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL‐based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools. [ABSTRACT FROM AUTHOR]

Abstract [Objective] WRKY transcription factors are involved in response to low phosphorus stress in plants. Based on transcriptome data of Tartary buckwheat (Fagopyrum tataricum ) under low phosphorus stress, the aim of this study is to isolate FtWRKY28 gene, to predict the structure of the gene and its deduced protein, to analyze the subcellular localization and transcription-activating activity, and to investigate gene expression in different organs under low phosphorus stress and hormone application, providing a basis for function identification of the gene. [Methods] Primer sequences were designed according to the Tartary buckwheat genome sequence. RT-PCR was used to amplify the CDS of FtWRKY28 from the RNAs generated from Tartary buckwheat roots at low phosphorus. Bioinformatical tools were employed to analyze the structure of the gene and protein and the phylogenetic relationship of the homologous proteins. qRT-PCR was used to investigate gene expression pattern. Transient expression system of the Arabidopsis protoplast was used to analyze the subcellular localization of the protein. Yeast-one-hybrid was employed to analyze the transcription-activating activity of the protein. [Results] The obtained CDS of Ft- WRKY28 was 876 bp in length, encoding a polypeptide of 291 amino acid residues consisting of one conserved WRKY domain with a zinc finger motif of C2H2, belonging to WRKY group Ⅱ. FtWRKY28 was predominantly localized in nucleus, which had transcription-activating activity. The transcript abundance of FtWRKY28 was relatively higher in roots, and was induced by low phosphorus and hormones such as indole acetic acid, gibberellin 3, and 6-benzylamino purine. [Conclusion] FtWRKY28 has basic structural and biochemical characteristics of a putative transcription factor, and involves in response to low phosphorus, which may crosstalk with auxin, gibberellin, and cytokinin signaling networks.

Abstract Oral poisoning can trigger diverse physiological reactions, determined by the toxic substance involved. One such consequence is hyperchloremia, characterized by an elevated level of chloride in the blood and leads to kidney damage and impairing chloride ion regulation. Here, we conducted a comprehensive genome-wide analysis to investigate genes or proteins linked to hyperchloremia. Our analysis included functional enrichment, protein–protein interactions, gene expression, exploration of molecular pathways, and the identification of potential shared genetic factors contributing to the development of hyperchloremia. Functional enrichment analysis revealed that oral poisoning owing hyperchloremia is associated with 4 proteins e.g. Kelch-like protein 3, Serine/threonine-protein kinase WNK4, Serine/threonine-protein kinase WNK1 and Cullin-3. The protein–protein interaction network revealed Cullin-3 as an exceptional protein, displaying a maximum connection of 18 nodes. Insufficient data from transcriptomic analysis indicates that there are lack of information having direct associations between these proteins and human-related functions to oral poisoning, hyperchloremia, or metabolic acidosis. The metabolic pathway of Cullin-3 protein revealed that the derivative is Sulfonamide which play role in, increasing urine output, and metabolic acidosis resulted in hypertension. Based on molecular docking results analysis it found that Cullin-3 proteins has the lowest binding energies score and being suitable proteins. Moreover, no major variations were observed in unbound Cullin-3 and all three peptide bound complexes shows that all systems remain compact during 50 ns simulations. The results of our study revealed Cullin-3 proteins be a strong foundation for the development of potential drug targets or biomarker for future studies.