Data Structure Research Articles

Determining the chemical ordering in nanoalloys by considering atomic coordination types.

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.

P2X7 receptors exhibit at least three modes of allosteric antagonism.

P2X receptors are trimeric ion channels activated by adenosine triphosphate (ATP) that contribute to pathophysiological processes ranging from asthma to neuropathic pain and neurodegeneration. A number of small-molecule antagonists have been identified for these important pharmaceutical targets. However, the molecular pharmacology of P2X receptors is poorly understood because of the chemically disparate nature of antagonists and their differential actions on the seven constituent subtypes. Here, we report high-resolution cryo-electron microscopy structures of the homomeric rat P2X7 receptor bound to five previously known small-molecule allosteric antagonists and a sixth antagonist that we identify. Our structural, biophysical, and electrophysiological data define the molecular determinants of allosteric antagonism in this pharmacologically relevant receptor, revealing three distinct classes of antagonists that we call shallow, deep, and starfish. Starfish binders, exemplified by the previously unidentified antagonist methyl blue, represent a unique class of inhibitors with distinct functional properties that could be exploited to develop potent P2X7 ligands with substantial clinical impact.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

PocketDTA: An advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets.

Accurately predicting the drug-target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of three-dimensional (3D) information and poor interpretability. To alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first three (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. Additionally, it employs a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability. Code and data are available at: Https://github.com/zhaolongNCU/PocketDTA.

Inference for bivariate extremes via a semi-parametric angular-radial model

AbstractThe modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for bivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series’, with results indicating generally good performance.

Advancing human mobility modeling: a novel path flow approach to mining traffic congestion dynamics

Mining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues.

Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder.

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.

PRC2-RNA interactions: Viewpoint from Tom Cech, Chen Davidovich, and Richard Jenner

Diverse biochemical, structural, and invivo data support models for the regulation of polycomb repressive complex 2 (PRC2) activity by RNAs, which may contribute to the maintenance of epigenetic states. Here, we summarize this research and also suggest why it can be difficult to capture biologically relevant PRC2-RNA interactions in living cells.

Tidysbml: R/bioconductor package for SBML extraction into dataframes

Abstract Summary We present tidysbml, an R package able to perform compartments, species and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3 and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome. Availability and implementation The tidysbml R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml). Supplementary information Supplementary Material is available at Bioinformatics Advances online.

Data reorganization for image encryption: presentation in the data of hare

ABSTRACT In track and field, a hare (or pacemaker) is a runner who leads the early part of a race to help other competitors achieve better performance and potentially set records. This practice became common in the early 1980s. In cryptography, organizing data before encryption serves as a strategic technique to strengthen defenses against cryptanalytic attacks. Attackers often exploit patterns in encrypted data to narrow down key possibilities. By deliberately disrupting data structure prior to encryption, we can complicate these efforts. This concept, termed “Presentation in the Data of Hare,” proposes various data organization methods (e.g., line by line, column by column) to enhance encryption. This approach can add 21 bits to the encryption key, significantly increasing the complexity and time required to crack it—equivalent to a 2 million-fold increase. This work focuses on improving security in cryptographic systems by employing innovative techniques, including those related to post-quantum cryptography. By strategically organizing data before encryption, we aim to hinder potential cryptanalytic attacks, making it considerably harder for attackers to exploit any regularities, thus bolstering resilience against both classical and quantum threats.

CoNglyPred: Accurate Prediction of N-Linked Glycosylation Sites Using ESM-2 and Structural Features With Graph Network and Co-Attention.

N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error.

MVSF-AB: Accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.

Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids' structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody-antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody-antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody-antigen Binding affinity prediction. MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody-antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody-antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies. Datasets we used and source code are available on our public GitHub repository https://github.com/TAI-Medical-Lab/MVSF-AB.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics.

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

B-119 Comparative performance of GPT-4 and CNV-ETLAI in extracting copy number variations from medical journals: Bridging the gap between large language models and specialized NLP tools in genomic data interpretation

Abstract Background Copy Number Variations (CNVs) are critical genetic markers in diversity and disease, yet their accurate extraction from medical literature remains challenging due to the complexity of genetic data. While specialized NLP models like CNV-ETLAI have been developed for this task, the advent of Large Language Models (LLMs) such as GPT-4 presents a potential alternative with broader applicability. This study evaluates the efficacy of GPT-4 against CNV-ETLAI in extracting CNVs from medical journal articles, aiming to enhance genetic research and clinical decision-making. Methods We configured GPT-4 to process and interpret medical journal PDFs, designing custom prompts for CNV information extraction. The performance of GPT-4 was benchmarked against CNV-ETLAI using a dataset of 146 true positive CNVs extracted from 23 journal articles. Performance metrics focused on accuracy in extracting CNVs from both text and tables, recognizing the importance of structured data interpretation in genomic analysis. Results CNV-ETLAI demonstrated superior accuracy, achieving a 98% success rate in CNV extraction, compared to GPT-4’s 49%. Specifically, CNV-ETLAI outperformed GPT-4 in table extraction accuracy (99% vs. 41.2%) and context extraction accuracy (96% vs. 63.2%). Despite GPT-4's lower performance, its capacity for improvement and adaptability was noted, indicating potential future applicability in medical data extraction. Conclusions The study highlights CNV-ETLAI's current superiority in extracting CNVs from medical texts, particularly in interpreting structured data like tables. However, the adaptability and potential for growth in LLMs like GPT-4 suggest they could soon become valuable tools for medical data extraction, offering a more versatile and powerful solution across a broader range of applications. The promise of LLMs, despite their current limitations, underscores the need for continued research and development in AI technologies for genomic data interpretation.

Bacillus subtilis EpsA-O: A novel exopolysaccharide structure acting as an efficient adhesive in biofilms

Extracellular polysaccharides are crucial components for biofilm development. Although Bacillus subtilis is one of the most characterized Gram-positive biofilm model system, the structure-function of its exopolysaccharide, EpsA-O, remains to be elucidated. By combining chemical analysis, NMR spectroscopy, rheology, and molecular modeling, high-resolution data of EpsA-O structure from atom to supramolecular scale was obtained. The repeating unit is composed of the trisaccharide backbone [→3)-β-d-QuipNAc4NAc-(1→3)-β-d-GalpNAc-(1→3)-α-d-GlcpNAc-(1]n, and the side chain β-d-Galp(3,4-S-Pyr)-(1→6)-β-d-Galp(3,4-S-Pyr)-(1→6)-α-d-Galp-(1→ linked to C4 of GalNAc. Close agreement between the primary structure and rheological behavior allowed us to model EpsA-O macromolecular and supramolecular solution structure, which can span the intercellular space forming a gel that leads to a complex 3D biofilm network as corroborated by a mutant strain with impaired ability to produce EpsA-O. This is a comprehensive structure-function investigation of the essential biofilm adhesive exopolysaccharide that will serve as a useful guide for future studies in biofilm architecture formation.

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Anomaly Detection over Streaming Graphs with Finger-Based Higher-Order Graph Sketch

A streaming graph is a constantly growing sequence of edges, which forms a dynamic graph that changes with every edge in the stream. An anomalous behavior in a streaming graph can be modeled as an edge or a subgraph that is unusual compared to the rest of the graph. Identifying anomalous behaviors in real time is essential to the early warning of abnormal or notable events. Due to the complexity of the problem, little work has been reported so far to solve the problem. In this paper, we propose Finger-based Higher-order Graph Sketch (FHGS for short), which is an approximate data structure for streaming graphs with linear memory usage, high update speed, and high accuracy and supports both edge and subgraph anomaly detection. FHGS first maps each edge into a matrix based on hash functions, and then counts its frequency in a time window with unique fingerprints for detecting anomalies. Extensive experiments confirm that our approach generate high-quality results compared to baseline methods.

Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra

The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a scheme to resolve powder diffraction data into crystal structures with precise atomic coordinates by screening the space of all possible atomic arrangements, i.e., structural prototypes, including those not previously observed, using a pre-trained machine learning (ML) model. This involves (i) enumerating all possible symmetry-confined ways in which a given composition can be accommodated in a given space group, (ii) ranking the element-assigned prototype representations using energies predicted using the Wyckoff representation regression ML model [Goodall , ], (iii) assigning and perturbing atoms along the degree of freedom allowed by the Wyckoff positions to match the experimental diffraction data, and (iv) validating the thermodynamic stability of the material using density-functional theory. An advantage of the presented method is that it does not rely on a database of previously observed prototypes and is, therefore capable of finding crystal structures with entirely new symmetric arrangements of atoms. We demonstrate the workflow on unidentified x-ray diffraction spectra from the ICDD database and identify a number of stable structures, where a majority turns out to be derivable from known prototypes. However, at least two are found not to be part of our prior structural data sets. Published by the American Physical Society 2024

Surface Chemical Effects on Fischer–Tropsch Iron Oxide Catalysts Caused by Alkali Ion (Li, Na, K, Cs) Doping

Among the alkali metals, potassium is known to significantly shift selectivity toward value-added, heavier alkanes and olefins in iron-based Fischer–Tropsch synthesis catalysts. The aim of the present contribution is to shed light on the mechanism of action of alkaline promoters through a systematic study of the structure–reactivity relationships of a series of Fe oxide FTS catalysts promoted with Group I (Li, Na, K, Cs) alkali elements. Reactivity data are compared to structural data based on in situ, synchrotron-based XRD and XPS, as well as temperature-programmed studies (TPR-H2, TPC-CO, TPD-CO2, and TPD-H). It has been observed that the alkali elements induced higher carburization rates, higher basicities, and lower adsorbed hydrogen coverages. Catalyst stability followed the trend Na-Fe > unpromoted > Li-Fe > K-Fe > Cs-Fe, being consistent with the ability of the alkali (Na) to prevent active site loss by catalyst reoxidation. Potassium was the most active in promoting high α hydrocarbon formation. It is active enough to promote CO dissociative adsorption (and the formation of FeCx active phases) and decrease the surface coverage of H-adsorbed species, but it is not so active as to cause premature catalyst deactivation by the formation of a carbon layer resulting in the blocking active sites.

Factors associated with physical and psychological wellness of American adults with asthma.

The study objective was to investigate the factors associated with physical and psychological wellness of United States (US) adults with asthma. This cross-sectional analysis used a sample of 2329 US adults with asthma in the 2021 Medical Expenditure Panel Survey data. A logistic regression model investigated the association of the following factors and the dependent variables (physical wellness and psychological wellness): age, sex, race, ethnicity, education, employment, healthcare provision, marriage, income, regular physical activity, current smoker, pain, and limitations. Nationally representative estimates were produced through a weighted analysis. The data structure was maintained using cluster and strata variables. The alpha limit was 0.05. Factors associated with higher odds of reporting good physical wellness included: private (versus no) healthcare provision (odds ratio [OR] = 2.63, 95% confidence interval [CI] = 1.10-6.26), good (versus poor) psychological wellness (OR = 6.83, 95% CI = 4.35-10.72), regular (versus no regular) physical activity (OR = 2.18, 95% CI = 1.42-3.34), little/moderate (versus quite a bit/extreme) pain (OR = 3.51, 95% CI = 2.38-5.15) and no (versus any) limitation (OR = 3.73, 95% CI = 2.30-6.06). In the psychological wellness model, those aged ≥70 (OR = 6.18, 95% CI = 2.72-14.07), 60-69 (OR = 4.64, 95% CI = 2.13-10.10), and 50-59 (OR = 4.96, 95% CI = 2.24-11.02) versus those aged 18-29, and good (versus poor) physical wellness (OR = 6.89, 95% CI = 4.34-10.94) were associated with higher odds of reporting good versus poor psychological wellness. In conclusion, these results may be helpful at targeting resources to optimize the wellness of US adults with asthma. Additional studies are needed to determine any temporal associations between these findings.