Structural Similarity Analysis Research Articles

Structural similarity of lithospheric velocity models of Chinese mainland

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features, with dissimilarities due to different data and methods used in model generation. The quantification of model structural similarity can help in interpreting the geophysical properties of Earth’s interior and establishing unified models crucial in natural hazard assessment and resource exploration. Here we employ the complex wavelet structural similarity index measure (CW-SSIM) active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade. We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling, which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution. Our results show that the CW-SSIM values vary in different model pairs, horizontal locations, and depths. While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation, the difference of tomography methods may significantly impact the similar structural features of models, such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China. We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.

Founder’s Syndrome

Why would establishing a business resemble starting a religion? When group relations start to break down, why might it feel like a cult? This article examines the blurred lines between religion and business through a focus on the small-scale action of individuals trying to start something that has both economic and spiritual aspects but does not neatly fit into either category. Ethnographically situated in participant observation fieldwork in Sedona, Arizona in 2013, this article focuses on an individual kung fu practitioner and teacher trying to establish a school for martial arts. A close examination of the failure of this attempt, and how many of the students became alienated in the process, leads to an analysis of structural similarities between new religions and startups. In the US, social groups that exist in capitalism will inevitably have economic aspects; those that are also religious face a double bind of this reality with cultural norms derived from a Protestant separation of economics and religion. This article seeks an analytic route beyond this double bind through examining how the corporate form of spiritual seminars shares structural similarities with startups.

CovalentInDB 2.0: an updated comprehensive database for structure-based and ligand-based covalent inhibitor design and screening.

The rational design of targeted covalent inhibitors (TCIs) has emerged as a powerful strategy in drug discovery, known for its ability to achieve strong binding affinity and prolonged target engagement. However, the development of covalent drugs is often challenged by the need to optimize both covalent warhead and non-covalent interactions, alongside the limitations of existing compound libraries. To address these challenges, we present CovalentInDB 2.0, an updated online database designed to support covalent drug discovery. This updated version includes 8303 inhibitors and 368 targets, supplemented by 3445 newly added cocrystal structures, providing detailed analyses of non-covalent interactions. Furthermore, we have employed an AI-based model to profile the ligandability of 144 864 cysteines across the human proteome. CovalentInDB 2.0 also features the largest covalent virtual screening library with 2 030 192 commercially available compounds and a natural product library with 105 901 molecules, crucial for covalent drug screening and discovery. To enhance the utility of these compounds, we performed structural similarity analysis and drug-likeness predictions. Additionally, a new user data upload feature enables efficient data contribution and continuous updates. CovalentInDB 2.0 is freely accessible at http://cadd.zju.edu.cn/cidb/.

Molecular structure similarity analysis using Tanimoto coefficient and its correlation analysis with Maltase-glucoamylase inhibitory activity of Nigella sativa’s compounds

Nigella sativa is one of the medicinal plants that are efficacious for treating diabetes mellitus. Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia due to damage of insulin action, insulin production, and/or both. In this study, the molecular structure similarity analysis of the compounds in Nigella sativa to acarbose and the correlation analysis of the similarity with its activity as antidiabetic with the mechanism of maltase glucoamylase (MGAM) inhibition was carried out. Similarity analysis has been done used Tanimoto coefficient. The prediction of MGAM inhibitory activity has been done using molecular docking with molegro virtual docker. The Kaempferol 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; (S)-2,3-Epoxysqualene; Quercetin 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; Oleic Acid has activity as an inhibitor of MGAM with rerank score -107.8770, -102.1760, -95.7338, -92.4246 respectively and these has Tanimoto score 0.426, 0.319, 0.413, 0.357 respectively. The correlation analysis obtained that there is a significant relationship between the Tanimoto Coefficient and Rerank Score with the opposite relationship because the correlation value is negative. Greater the degree of molecular structure similarity of Nigella sativa’s compounds to acarbose more likely has the similar biological activity as MGAM inhibitory

Navigating in harmony: Investigating spatial correlations between cetacean distribution and marine traffic in the Santos Basin, Brazil

Cetaceans inhabit oceans worldwide, coexisting amidst the growing intensity of human activities in the aquatic environment, particularly motorized vessel traffic. Understanding spatial correlations is essential for devising appropriate responses to these impacts. Hence, this project delved into the spatial relationship between cetacean distribution and marine traffic activities in the Santos Basin, Brazil. Data from two different databases were collected: one detailing cetacean distribution and the other to documenting various types of marine traffic in general. A total of 2352 cetacean records and 718777 vessel records were obtained. Subsequently, Structural Similarity analyses were carried out to identify niche overlap, yielding a result of 0.88. Addition, analysis utilizing the Nearest Neighbor method and Kernel density were employed, to pinpoint overlapping records of marine traffic with cetaceans, generating heat maps. These findings facilitated the identification of the areas where marine traffic intersects with cetacean habitats, leading to the proposal of both mandatory and voluntary conservation strategies. Key recommendations include speed reduction and voluntary exclusion zones. It is anticipated that the implementation of these strategies in the study area will aid in the conservation of cetaceans and the broader biodiversity of Brazilian Marine Protected Areas.

Qualitative and quantitative analysis of pesticide isomers based on collision-induced dissociation of high-resolution mass spectrometry

Pesticide isomers are widely present in agricultural production, while their biological activity, efficacy, and toxicity may vary significantly. Due to structural similarity, chromatographic and mass spectrometric analysis of isomeric pesticides are challenging. In this experiment, representative isomeric pesticides with great differences in toxicity and maximum residue limits among countries were studied. Based on collision-induced dissociation (CID) of high-resolution mass spectrometry, the fragmentation pathways of substituent position isomer, functional group position isomer, carbon frame isomer, functional group isomer, and cis–trans isomer were analyzed to attribute fragment ions. In addition, for the isomers with the same product ions and different abundance ratios, the abundance differences at different energy-resolved mass spectrometry were analyzed, which was combined with the computational chemical density functional theory to simulate the specific process of fragmentation and to explain the reasons for the differences in fragment ions and abundance ratios. Based on the above study, a high-resolution mass spectrometry method was established for the separation and analysis of pesticide isomers. Twelve pesticide isomers were distinguished by retention time, fragment ions, and relative abundance ratio. This method was applied to the detection of real fruit and vegetable samples.

A Search for Sars‐CoV‐2 Main Protease Inhibitors: Synthesis and Docking Study of Steroidal Dinitriles

AbstractCoronaviruses are continually evolving, as evidenced by the emergence of three closely related pathogenic variants over the past two decades: SARS (2002), MERS (2012), and SARS‐CoV‐2 (2019); and the appearance of many new SARS‐CoV‐2 strains during the COVID‐19 pandemic. Hence, COVID‐19 has now become a pandemic disease; with a real possibility for the emergence of new deadly SARS‐related coronaviruses. The SARS‐CoV‐2 main protease, Mpro is an attractive drug target for the development of inhibitors against SARS type and other coronaviruses because it is essential for the viral life cycle and highly conserved among a wide range of pathogenic coronaviruses. This motivated the synthesis of novel inhibitors of Mpro, which could be the basis for COVID‐19 drug development. The synthesis of new 5,6 : 16,17‐diseco and 6,7 : 16,17‐disecoandrostane 16,17a‐dinitriles is reported. In silico ADMET analyses suggest that the synthesized compounds possess drug‐like properties with no predicted toxicities. Structural similarity analyses indicates that synthesized B,D‐diseco 16,17a‐dinitrile derivatives have potential as protease inhibitors. Molecular docking simulations suggest that B,D‐diseco 16,17a‐dinitriles could inhibit substrate binding by Mpro. The present study suggests that newly synthesized androstane 5,6 : 16,17‐diseco‐5‐keto‐6‐oic acid and 6,7 : 16,17‐diseco‐6,7‐dioic acid 16,17a‐dinitriles 8 and 11 could represent a starting point for the design of Mpro inhibitors against SARS type coronaviruses.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and correlations from transcriptome and metabolome analysis, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.

In silico approach of 2,5-Diketopiperazines from marine organisms to neurodegenerative diseases

In this paper we present an in silico studies of new biological active of 2,5-Diketopiperazines candidates, from Marine Organisms to neurodegenerative diseases particular for the neurodegenerative central nervous system to Alzheimer's, Huntington and Parkinson’s diseases. A total of 35 DKPs were studied, by structural similarity analysis obtained MAO-A/B, β/γ-Secretase, COX-1/2 enzymes targets obtained, to continue molecular docking studies compared to endogenous substrates and reference inhibitors, finding a multitarget potential to increase dopamine levels and decrease β-amyloid and PGE2 levels, which makes them excellent molecules for studies against neurodegenerative diseases. DKP4, DKP23 and DKP25 as inhibitors of the 6 enzymes and DKP15, DKP19, DKP21, DKP26 and DKP33 for β-Secretease specifically, the rest with multitarget potential, denoting that the DKP ring serves as a base to generate multitarget or unitarget compounds through modifications in substituents. Finally, DKPs present low bioaccumulation in the body, no toxicity, high feasibility of crossing hematoencephalic membrane and activity on the CNS, which makes them an interesting set of molecules for the search for alternatives against neurodegenerative diseases.

Structure-guided discovery of anti-CRISPR and anti-phage defense proteins

Bacteria use a variety of defense systems to protect themselves from phage infection. In turn, phages have evolved diverse counter-defense measures to overcome host defenses. Here, we use protein structural similarity and gene co-occurrence analyses to screen >66 million viral protein sequences and >330,000 metagenome-assembled genomes for the identification of anti-phage and counter-defense systems. We predict structures for ~300,000 proteins and perform large-scale, pairwise comparison to known anti-CRISPR (Acr) and anti-phage proteins to identify structural homologs that otherwise may not be uncovered using primary sequence search. This way, we identify a Bacteroidota phage Acr protein that inhibits Cas12a, and an Akkermansia muciniphila anti-phage defense protein, termed BxaP. Gene bxaP is found in loci encoding Bacteriophage Exclusion (BREX) and restriction-modification defense systems, but confers immunity independently. Our work highlights the advantage of combining protein structural features and gene co-localization information in studying host-phage interactions.

Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications.

Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications.

Ultrasound-assisted green synthesis and in silico study of 6-(4-(butylamino)-6-(diethylamino)-1,3,5-triazin-2-yl)oxypyridazine derivatives

Abstract Eco-friendly ultrasound-assisted synthesis of a series of 3-N-substituted 6-((4-(butylamino)-6-(diethylamino)-1,3,5-triazin-2-yl)oxy)pyridazin-3(2H)-one derivatives and in silico study to predict their biological activities were carried out. Physicochemical and pharmacokinetic properties were obtained. Absorption, distribution, metabolism, excretion and toxicity properties and bioavailability index were calculated. A comparative analysis of structural similarity based on the Tanimoto coefficient was carried out.

Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein.

In this in silico study, we conducted an in-depth exploration of the potential of natural products and antihypertensive molecules that could serve as inhibitors targeting the key proteins of the SARS-CoV-2 virus: the main protease (Mpro) and the spike (S) protein. By utilizing Induced Fit Docking (IFD), we assessed the binding affinities of the molecules under study to these crucial viral components. To further comprehend the stability and molecular interactions of the "protein-ligand" complexes that derived from docking studies, we performed molecular dynamics (MD) simulations, shedding light on the molecular basis of potential drug candidates for COVID-19 treatment. Moreover, we employed Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations on all "protein-ligand" complexes, underscoring the robust binding capabilities of rosmarinic acid, curcumin, and quercetin against Mpro, and salvianolic acid b, rosmarinic acid, and quercetin toward the S protein. Furthermore, in order to expand our search for potent inhibitors, we conducted a structure similarity analysis, using the Enalos Suite, based on the molecules that indicated the most favored results in the in silico studies. The Enalos Suite generated 115 structurally similar compounds to salvianolic acid, rosmarinic acid, and quercetin. These compounds underwent IFD calculations, leading to the identification of two salvianolic acid analogues that exhibited strong binding to all the examined binding sites in both proteins, showcasing their potential as multi-target inhibitors. These findings introduce exciting possibilities for the development of novel therapeutic agents aiming to effectively disrupt the SARS-CoV-2 virus lifecycle.

Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism.

The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E-I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.

Identification of dietary compounds that interact with the circadian clock machinery: Molecular docking and structural similarity analysis

The molecular clock is vital for regulating circadian rhythms in various physiological processes, and its dysregulation is associated with multiple diseases. As such, the use of small molecule modulators to regulate the molecular clock presents a promising therapeutic approach. In this study, we generated a homology model of the human circadian locomotor output cycles kaput (CLOCK) protein to evaluate its ligand binding sites. Using molecular docking, we obtained further insights into the binding mode of the control compound CLK8 and explored a selection of dietary compounds. Our investigation of dietary compounds was guided by their potential interactions with the retinoic acid-related orphan receptors RORα/γ, which are involved in circadian regulation. Through the molecular similarity and docking analyses, we identified oleanolic acid demethyl, 3-epi-lupeol, and taraxasterol as potential ROR-interacting compounds. These compounds may exert therapeutic effects through their modulation of RORα/γ activity and subsequently influence the molecular clock. Overall, our study highlights the potential of small molecule modulators in regulating the molecular clock and the importance of exploring dietary compounds as a source of such modulators. Our findings also provide insights into the binding mechanisms of CLK8 and shed light on potential compounds that can interact with RORs to regulate the molecular clock. Future investigations could focus on validating the efficacy of these compounds in modulating the molecular clock and their potential use as therapeutic agents.

Main path analysis considering citation structure and content: Case studies in different domains

Main path analysis (MPA) is an effective method widely accepted in science and technology for extracting knowledge diffusion paths. Traditional citation analysis assumes that all citations are treated equally. In contrast, this paper proposes a new MPA framework from the perspective of citation structure and content. Three indicators are considered to adjust edge weight: (1) Structural similarity, (2) Topic similarity and (3) Sentiment analysis. This study takes the bullwhip effect and the Internet of Things domain as examples to verify the reliability and feasibility of improved MPA. The results show that the improved main path uncovers the knowledge trajectories appropriately, which has an ability to distinguish citations and detect important papers. This research enriches MPA theory and provides future research directions from perspective of citation structure and content.

Abstract 1738: Analysis of structural similarities between TAM receptors and other cell surface molecules

Abstract 1738: Analysis of structural similarities between TAM receptors and other cell surface molecules

In Vitro Assessment on Designing Novel Antibiofilms of Pseudomonas aeruginosa Using a Computational Approach.

An anti-biofilm that can inhibit the matrix of biofilm formation is necessary to prevent recurrent and chronic Pseudomonas aeruginosa infection. This study aimed to design compounds with a new mechanism through competitive inhibitory activity against phosphomannomutase/phosphoglucomutase (PMM/PGM), using in vitro assessment and a computational (in silico) approach. The active site of PMM/PGM was assessed through molecular redocking using L-tartaric acid as the native ligand and other small molecules, such as glucaric acid, D-sorbitol, and ascorbic acid. The docking program set the small molecules to the active site, showing a stable complex formation. Analysis of structural similarity, bioavailability, absorption, distribution, metabolism, excretion, and toxicity properties proved the potential application of ligands as an anti-biofilm. In vitro assessment with crystal violet showed that the ligands could reach up to 95.87% inhibition at different concentrations. The nitrocellulose membrane and scanning electron microscopic visualization showed that the untreated P. aeruginosa biofilm was denser than the ligand-treated biofilm.

Statistical analysis and visualization of data of non-fullerene small molecule acceptors from Harvard organic photovoltaic database. Structural similarity analysis with famous non-fullerene small molecule acceptors to search new building blocks

Data-driven material design has gained the position of “fourth paradigm” with the first three being experiments, theory, and simulation. The statistical analysis and visualization of data can provide many information about hidden trends of data. It also can help to plan future material design in efficient way. Traditional methods need long experimental time and much money. Data driven research has become new hope to revolutionize materials discovery. A detail statistical and visualization analysis of Harvard organic photovoltaic database is performed. Various new information is found. Similarity analysis is also performed. Famous experimentally reported non-fullerene small acceptors are selected as reference molecules. Very less similarity is found that is indicating that acceptors in Harvard organic photovoltaic database are much different from acceptors that are reported in literature. The molecules in database are not suitable acceptors, however, their suitable combination can result efficient acceptors of organic solar cells. New acceptors are designed using some building block from database and their electronic properties are studied using density functional theory. Our data mining assisted material design framework is based on unique approach that is rare in photovoltaics research. This framework will be helpful to find versatile building blocks.