Structural Database Research Articles

VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein–ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein–ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein–ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein–ligand complex, and could be expanded to screen any database of protein structures.

A Survey on Relational Database Based Multi Relational Classification Algorithms

Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge. This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.

Physics-Informed Generative Model for Drug-like Molecule Conformers.

We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.

Information System for the Selection of Hydropneumatic Capacitive Vessels

This article describes an algorithm for the automatic selection of hydropneumatic capacitive vessels used for household and industrial purposes. The suggested informational system makes it possible to reduce the working time spent on the selection of equipment and increase the relevance of the analyzed information. The article discusses a detailed description of the design of pneumohydroaccumulators, as well as provides examples of calculations of their main characteristics. The main task we needed to solve was a multi-criteria choice, and the article provides an overview of methods for solving this problem. In addition to these methods, we analyzed information systems that allow one to select suitable equipment among various alternatives. Based on the analysis of methods and similar software solutions, the authors suggested an information system and an algorithm for automatic selection of hydraulic accumulators by searching for options that meet the parameters specified by the system user. Also, the article discusses the process of purchasing capacitive equipment. There was demonstrated a logical model of the database structure storing the technical characteristics of the equipment which reflects objects of the information system and their connections. In this article we presented and argued the software for development of a specified information system. The development of the information system was based on the "1С platform: The enterprise". The task of selecting hydropneumatic capacitive vessels was solved by a request that can be interacted with through a report in the information system. This report allows the user to search for equipment by quantitative and qualitative characteristics: volume, operating pressure, vessel material, flange material, installation type (vertical or horizontal), availability of legs, cost, manufacturer or supplier. The report options allow users to search for equipment both in accordance with the specified parameters and with a deviation from them but only if the parameter is quantitative. The results of the report are ordered by relevance to the criteria specified by the user. The operation of the report query aggregating data was reflected in the flowchart. The article presents a situation where the user solves the selection problem by using capabilities of the report. The problem of multi-criteria selection was formulated in a way to demonstrate how a clear parameter of the report becomes fuzzy, and our solution of it allows you to find more alternative options with sorting by relevance helping you to identify the most suitable pneumohydroaccumulator. This software package was registered in the state registry of computer programs.

The potential of honey from Apis cerana in preventing chronic inflammation by downregulating the NF-?B pathway

Chronic inflammation is an inflammatory condition that persists over a long period of time causing tissue damage and contributing to the development of chronic diseases such as arthritis, heart disease, or certain autoimmune conditions. The protein that plays a role in this regulation is the up-regulation of NF-?B, this is the aim of this research, namely to determine the potential of the compounds contained in A.cerana honey to bind with NF-?B. The research stages carried out were preparing the NF-?B protein structure database, protein preparation using the Yasara application, structure preparation and optimization using the Chemaxon application, as well as validating the molecular docking method and docking of compounds from A.cerana honey on the NF-?B protein using the PLANT application. The research results show that Ellagic acid, Taxifolin, Quercetin and Chrysin have affinity and form hydrogen bonds with the NF-?B protein. The binding energy between Ellagic acid, Taxifolin, Quercetin and Chrysin with the NF-?B protein respectively is 78,822; -64.89; -64,872 and -64,935. Ellagic acid, Taxifolin, Quercetin and Chrysin have potential anti-inflammatory activity through binding to the NF-?B protein, thereby preventing the inflammatory process.

Crystal structure of tetra-phenyl phosphate tetra-kis-[dimethyl (2,2,2-tri-chloro-acet-yl)phos-pho-ramidato]lutetium(III), PPh4[LuL4

A lutetium(III) complex based on the anion of the ligand dimethyl (2,2,2-tri-chloro-acet-yl)phospho-ramidate (HL) and tetra-phenylphosphonium, of composition PPh4[LuL 4] (L = CAPh = carbacyl-amido-phosphate), or (C24H20)[Lu(C4H6Cl3NO4P)4], has been synthesized and structurally characterized. The X-ray diffraction study of the compound revealed that the lutetium ion is surrounded by four bis-chelating CAPh ligands, forming the complex anion [LuL 4]- with a coordination number of 8[O] for LuIII, while PPh4 + serves as a counter-ion. The coordination geometry around the Lu3+ ion was determined to be a nearly perfect triangular dodeca-hedron. The complex crystallizes in the monoclinic crystal system, space group P21/c, with four mol-ecules in the unit cell. Weak hydrogen bonds O⋯HC(Ph), Cl⋯HC(Ph) and N⋯HC(Ph) are formed between the cations and anions. For a comparative study, HL-based structures were retrieved from the Cambridge Structural Database (CSD) and their geometries and conformations are discussed. A Hirshfeld surface analysis was also performed.

Escherichia coli research on Raman measurement mechanism and diagnostic model

Escherichia coli (E. coli) is one of the most important pathogenic bacteria causing poultry diseases, characterized by a wide distribution range, rapid spread, and high mortality rate. Early diagnosis of E. coli in poultry feces provides the possibility for targeted treatment and rapid recovery of diseased poultry, and more importantly, prevents the rapid spread of pathogens among densely bred poultry. In order to implement rapid, low-cost, and high-frequency detection of E. coli, this study explored the feasibility of Raman spectroscopy. Firstly, theoretical configurations and density functional calculations of N-acetylmuramic acid and N-acetylglucosamine in the cell wall of E. coli were performed. Then, Raman measurement models for E. coli were established based on two feature extraction methods (Successive Projections Algorithm, Competitive Adaptive Reweighted Sampling) and four modeling methods (Random Forest Algorithm, Convolutional Neural Networks, Back Propagation Neural Networks, Radial Basis Function). Finally, a method based on the extraction of Raman spectral features using density functional theory was determined to optimize the existing models, and it was demonstrated that this feature variable extraction method improved the accuracy of all four measurement models to some extent. Ultimately, the optimal model, the improved SPA-RF, was obtained through comparative analysis, with an accuracy, precision, recall, specificity, FNR, FDR, and AUC of 98.38%, 98.61%, 99.83%, 88.08%, 0.81%, 11.82%, and 1, respectively. This study reports an early method for the early treatment of E. coli diseases and provides a molecular structure database for studying N-acetylmuramic acid and N-acetylglucosamine, as well as a basis for vibrational spectroscopy detection of E. coli diseases, promoting the application of Raman spectroscopy technology in the diagnosis of livestock diseases.

Machine learning-based prediction of proteins’ architecture using sequences of amino acids and structural alphabets

In addition to the growth of protein structures generated through wet laboratory experiments and deposited in the PDB repository, AlphaFold predictions have significantly contributed to the creation of a much larger database of protein structures. Annotating such a vast number of structures has become an increasingly challenging task. CATH is widely recognized as one the most common platforms for addressing this challenge, as it classifies proteins based on their structural and evolutionary relationships, offering the scientific community an invaluable resource for uncovering various properties, including functional annotations. While CATH annotation involves – to some extent – human intervention, keeping up with the classification of the rapidly expanding repositories of protein structures has become exceedingly difficult. Therefore, there is a pressing need for a fully automated approach. On the other hand, the abundance of protein sequences stemming from next generation sequencing technologies, lacking structural annotations, presents an additional challenge to the scientific community. Consequently, 'pre-annotating’ protein sequences with structural features, ensuring a high level of precision, could prove highly advantageous. In this paper, after a thorough investigation, we introduce a novel machine-learning model capable of classifying any protein domain, whether it has a known structure or not, into one of the 40 main CATH Architectures. We achieve an F1 Score of 0.92 using only the amino acid sequence and a score of 0.94 using both the sequence of amino acids and the sequence of structural alphabets. Communicated by Ramaswamy H. Sarma

Disentangling Ancestral State Reconstruction in historical linguistics

Abstract Ancestral State Reconstruction (ASR) is an essential part of historical linguistics (HL). Conventional ASR in HL relies on three core principles: fewest changes on the tree, plausibility of changes and plausibility of the resulting combinations of features in proto-languages. This approach has some problems, in particular the definition of what is plausible and the disregard for branch lengths. This study compares the classic approach of ASR to computational tools (Maximum Parsimony and Maximum Likelihood), conceptually and practically. Computational models have the advantage of being more transparent, consistent and replicable, and the disadvantage of lacking nuanced knowledge and context. Using data from the structural database Grambank, I compare reconstructions of the grammar of ancestral Oceanic languages from the HL literature to those achieved by computational means. The results show that there is a high degree of agreement between manual and computational approaches, with a tendency for classical HL to ignore branch lengths. Explicitly taking branch lengths into account is more conceptually sound; as such the field of HL should engage in improving methods in this direction. A combination of computational methods and qualitative knowledge is possible in the future and would be of great benefit.

Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning

Abstract Objectives Federated learning (FL) is a group of methodologies where statistical modelling can be performed without exchanging identifiable patient data between cooperating institutions. To realize its potential for AI development on clinical data, a number of bottlenecks need to be addressed. One of these is making data Findable-Accessible-Interoperable-Reusable (FAIR). The primary aim of this work is to show that tools making data FAIR allow consortia to collaborate on privacy-aware data exploration, data visualization, and training of models on each other’s original data. Methods We propose a “Schema-on-Read” FAIR-ification method that adapts for different (re)analyses without needing to change the underlying original data. The procedure involves (1) decoupling the contents of the data from its schema and database structure, (2) annotation with semantic ontologies as a metadata layer, and (3) readout using semantic queries. Open-source tools are given as Docker containers to help local investigators prepare their data on-premises. Results We created a federated privacy-preserving visualization dashboard for case mix exploration of 5 distributed datasets with no common schema at the point of origin. We demonstrated robust and flexible prognostication model development and validation, linking together different data sources—clinical risk factors and radiomics. Conclusions Our procedure leads to successful (re)use of data in FL-based consortia without the need to impose a common schema at every point of origin of data. Advances in knowledge This work supports the adoption of FL within the healthcare AI community by sharing means to make data more FAIR.

The Cambridge Structural Database and structural dynamics.

With the availability of the computer readable information in the Cambridge Structural Database (CSD), wide ranging, largely automated comparisons of fragment, molecular, and crystal structures have become possible. They show that the distributions of interatomic distances, angles, and torsion angles for a given structural fragment occurring in different environments are highly correlated among themselves and with other observables such as spectroscopic signals, reaction and activation energies. The correlations often extend continuously over large ranges of parameter values. They are reminiscent of bond breaking and forming reactions, polyhedral rearrangements, and conformational changes. They map-qualitatively-the regions of the structural parameter space in which molecular dynamics take place, namely, the low energy regions of the respective (free) energy surfaces. The extension and continuous nature of the correlations provides an organizing principle of large groups of structural data and suggests a reconsideration of traditional definitions and descriptions of bonds, "nonbonded" and "noncovalent" interactions in terms of Lewis acids interacting with Lewis bases. These aspects are illustrated with selected examples of historic importance and with some later developments. It seems that the amount of information in the CSD (and other structural databases) and the knowledge on the nature of, and the correlations within, this body of information should allow one-in the near future-to make credible interpolations and possibly predictions of structures and their properties with machine learning methods.

Analysis of Mineralization and Disease Characteristics of Bronzes Unearthed in Archaeology

Bronze artifacts unearthed by archaeologists are affected by underground burial environment and other factors, and most of them have different degrees of corrosion. Bronze artifacts unearthed by archaeologists have different degrees of mineralization. Establishing a data analysis database for basic structure and disease analysis of mineralized bronze artifacts is a necessary prerequisite for developing targeted reinforcement materials and realizing accurate protection. Based on the characteristic analysis of the mineralized bronzes, this paper systematically analyzed the mineralization and disease characteristics of the mineralized bronzes by Confocal Super Depth-of-Field Microscope(CSDTM), scanning electron microscopy (SEM), energy spectrum analysis(EDS) and XRD, and compared and analyzed the microscopic morphology, composition and phase structure of the bronzes under the same burial environment, so as to provide support for subsequent targeted protection and reinforcement.

AlphaFold for a medicinal chemist: tool or toy?

The development of novel small drug molecules is a complex and important cross-disciplinary task. In the early stages of development, chemoinformatics and bioinformatics methods are routinely used to reduce the cost of finding a lead compound. Among the tools of medicinal chemistry, docking and molecular dynamics occupy a special place. These methods are used to predict the possible mechanism of binding of a potential ligand to a protein target. However, in order to perform a docking study, it is necessary to know the spatial structure of the protein under investigation. Although databases of crystallographic structures are available, the three-dimensional representations of many protein molecules have not been reported. There is therefore a need to model such three-dimensional conformations. Several computer algorithms have been published to solve this problem. AlphaFold is considered by the scientific community to be the most effective approach to predicting the three-dimensional structure of proteins. However, the scope of its application in medicinal chemistry, especially for virtual screening, remains unclear. This review describes methods for predicting the three-dimensional structure of a protein and provides representative examples of the use of AlphaFold for the design and rational selection of potential ligands. Special attention is given to publications presenting the results of experimental validation of the approach. On the basis of performed analysis, the main problems in the field and possible ways to solve them are formulated.<br> The bibliography includes 154 references.

INELASTIC DISPLACEMENTS MACHINE-LEARNING-BASED ESTIMATIONS FOR THE RC-BUILDINGS SEISMIC DESIGN CONSIDERING SSI EFFECTS

Over the past few decades, scientists have dedicated considerable attention to investigating the impact of soil-structure interaction (SSI) on buildings’ performance. It is now widely recognized that these interaction effects can either positively or negatively influence a building's response to seismic events. Regarding the seismic design of buildings following standard prescriptions and accounting for SSI effects, ASCE 7, for example, guides through the modification process of the design forces obeying the interacting soil-structure system (SSS) behavior in an oversimplified manner. Nevertheless, no recommendations are made for adequately estimating inelastic displacements (IDs) focused on the design process. In an effort to rectify these limitations, this study examines the inelastic displacement response of ductile RC buildings while incorporating the influence of SSI. In that sense, a database of 3D-model structures with varying in-plan and elevation geometries as well as different supporting soil characteristics were generated and later assessed using OpenSeesPy as the modeling and analysis engine. Nonlinear dynamic analyses were executed accounting for flexible-base and fixed-base conditions as per ASCE 41, and their inelastic displacements are used to generate estimation models. The Gradient Boosting Regression Tree (GBRT) technique from machine learning (ML) is used in accomplishing this aim. It was observed that the wave parameter σ, along with the flexible-to-fixed base design shear force ratio, V*, are enough to explain up to 90% of the variation in IDs in the estimation model.

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data.

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.

ClusterFinder: a fast tool to find cluster structures from pair distribution function data.

A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 104 to 105 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal-oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.

Comparative Conformational Analysis of Acyclic Sugar Alcohols Ribitol, Xylitol and d-Arabitol by Solution NMR and Molecular Dynamics Simulations.

Ribitol (C5H12O5) is an acyclic sugar alcohol that was recently identified in O-mannose glycan on mammalian α-dystroglycan. The conformation and dynamics of acyclic sugar alcohols such as ribitol are dependent on the stereochemistry of the hydroxyl groups; however, the dynamics are not fully understood. To gain insights into the conformation and dynamics of sugar alcohols, we carried out comparative analyses of ribitol, d-arabitol and xylitol by a crystal structure database search, solution NMR analysis and molecular dynamics (MD) simulations. The crystal structures of the sugar alcohols showed a limited number of conformations, suggesting that only certain stable conformations are prevalent among all possible conformations. The three-bond scholar coupling constants and exchange rates of hydroxyl protons were measured to obtain information on the backbone torsion angle and possible hydrogen bonding of each hydroxyl group. The 100 ns MD simulations indicate that the ribitol backbone has frequent conformational transitions with torsion angles between 180∘ and ±60∘, while d-arabitol and xylitol showed fewer conformational transitions. Taking our experimental and computational data together, it can be concluded that ribitol is more flexible than d-arabitol or xylitol, and the flexibility is at least in part defined by the configuration of the OH groups, which may form intramolecular hydrogen bonds.

WEBSITE SISTEM PAKAR DIAGNOSA PENYAKIT KULIT DENGAN FORWARD CHAINING (STUDI KASUS : PUSKESMAS TANGGULANGIN)

A website-based expert system for diagnosing skin diseases using the Forward Chaining method and the Laravel Framework at the Tanggulangin Health Center. The main focus of research is to improve accessibility, accuracy of diagnosis, and public awareness of skin health. Quantitative approaches and exploratory design are applied through literature studies, interviews, surveys, system design, trials, evaluations, and improvements. The system development process involves collecting data from observations, interviews, and medical data to devise relational rules, tables of disease types, symptoms, and decision trees. The use of the Laravel Framework is the main foundation of system design, including user interface (UI) design, database structure, Forward Chaining inference logic, and diagnostic needs. System trials are conducted with test scenarios to evaluate the system's ability to diagnose various skin disease conditions. Data analysis is carried out quantitatively, especially on test results, to assess system reliability. The results of this study provide an empirical basis for recommendations for further development. Hopefully, this system can make a positive contribution in the process of diagnosing skin diseases at the Tanggulangin Health Center and increase overall public skin health awareness. This system is able to provide a diagnosis of skin diseases based on the symptoms inputted by the user. The evaluation carried out provides a positive picture regarding the effectiveness and satisfaction of users.

The design of relational database and NoSQL database in travel agency database system

The information management systems of travel companies are experiencing greater challenges and expectations as a result of the tourism industrys rapid growth and travellers rising demand for personalized and interactive experiences. This paper aims to study the customer management system of travel agencies, taking into full consideration the characteristics and development trends of the tourism industry. Traditional customer management system uses relational database for data storage and related operations, which cannot fully meet the needs of customers and complex data in the new era. By analysing and comparing relational databases and NoSQL databases, according to their own advantages, a combined solution is proposed, which uses relational databases to store structured data and NoSQL databases to store unstructured data. On the basis of maintaining the original system management function, this solution greatly enhances the storage and processing of interactive data such as images and videos, and better meets the new demand of travel agency customers for sharing and interactive communication.

Glycerol as Ligand in Metal Complexes—A Structural Review

The molecule glycerol (H3gly) plays a key role in sustainable and green chemistry. Having been discovered for over 200 years and produced from vegetable oils and animal fats by hydrolysis, saponification and transesterification reactions, this natural triol is today employed in a wide range of cosmetic, food, polymer and pharmaceutical applications. Moreover, it is an essential C3 precursor in the chemical industry, used in the production of several intermediates and it avoids the need for petroleum-based precursors. Less famous but just as exciting, in the domain of coordination chemistry, glycerol is also proving to be a suitable ligand, capable of binding to one or more metal centres, either directly in its triol H3gly form (rather rare), or in its various deprotonated glycerolate forms, such as [H2gly]−, [Hgly]2− and [gly]3− (in most cases). Since the 1970s, various molecular structures prepared from glycerol and metallic and organometallic precursors, ranging from mononuclear complexes to sophisticated aggregates and coordination polymers, have been isolated and characterised. On the basis of the single-crystal X-ray diffraction structures described so far in the literature and deposited in the Cambridge Structural Database, in this structural inventory, we review the different modes of coordination of glycerol and glycerolates with metals.