Comparative genomic assessment of members of genus Tenacibaculum: an exploratory study.

Tenacibaculosis is an ulcerative skin disorder that affects finfish. It is caused by members of the genus Tenacibaculum, resulting in eccentric behavioural changes, including anorexia, lethargy, and abnormal swimming patterns that often result in mortality. Currently, species suspected of causing fish mortality include T. ovolyticum, T. gallaicum, T. discolor, T. finnmarkense, T. mesophilum, T. soleae, T. dicentrarchi, and T. maritimum. However, pathogenic members and the mechanisms involved in disease causation, progression, and transmission are limited due to the inadequate sequencing efforts in the past decade. In this study, we use a comparative genomics approach to investigate the characteristic features of 26 publicly available genomes of Tenacibaculum and report our observations. We propose the reclassification of "T. litoreum HSC 22" to the singaporense species and assignment of "T. sp. 4G03" to the species discolor (species with quotation marks have not been appropriately named). We also report the co-occurrence of several antimicrobial resistance/virulence genes and genes private to a few members. Finally, we mine several non-B DNA forming regions, operons, tandem repeats, high-confidence putative effector proteins, and sortase that might play a pivotal role in bacterial evolution, transcription, and pathogenesis.

Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: a multisampling algorithm-based structural study.

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were - 6.884 kcal/mol, - 7.515 kcal/mol, and - 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein-ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance.

Structure-Based Design of Novel MAO-B Inhibitors: A Review.

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors.

DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study.

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking, DFT, and MD Simulation Analysis.

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of -5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a -4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO-LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.

Filgrastim loading in PLGA and SLN nanoparticulate system: a bioinformatics approach.

OBJECTIVE: In this research work, we hypothesized to predict the nanoparticulate system, best suited for targeted delivery of filgrastim. Significance: Targeted delivery of filgrastim to bone marrow is required to decrease the incidence of neutropenia/febrile neutropenia. This is achieved by nanoparticulate systems, duly designed by bioinformatics approach. METHOD: The targeted delivery of filgrastim in nanoparticulate system was achieved by molecular dynamics (MD) simulation studies. Two matrices comprising PLGA and SLN (tripalmitin, core component of SLN system) were modeled separately with proposed drug filgrastim. Energy minimization of all systems was done using the steepest descent method. PLGA and tripalmitin systems were equalized at 310 °C, at 1 bar pressure with Berendsen barostat for 200 ps using a v-rescale thermostat for 100 ps. Atomistic MD simulations of four model system and mass density of interacting systems were calculated. RESULTS: The mass density maps of each nanoparticle system, that is, PLGA and tripalmitin showed an increase in density toward the end of the simulation. The contact numbers attained equilibria with the average number of approx.. 1500 contacts in case of tripalmitin-filgrastim system. While PLGA-filgrastim system shows lesser contacts as compared to tripalmitin with average contacts of approx. 1000.The binding free energy was predicted to be -1104 kJ/mol in tripalmitin-filgrastim complex and -421 kJ/mol in PLGA-filgrastim system. CONCLUSION: Findings of study revealed that both nanoparticle systems assumed to be good model for drug-carrier systems. Though SLN systems were thought to be more appropriate than PLGA, still the in vivo findings could ascertain this hypothesis in futuristic work.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: an optimisation followed multi-simulation and in-vitro study.

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer cells become resistant to the drug, making it less effective leaves the patient in vulnerable conditions. To overcome this situation, researchers are constantly working on new drugs and medications that can help fight drug resistance and improve patients' outcomes. In this study, we have taken five main proteins of lung cancer, namely RSK4 N-terminal kinase, guanylate kinase, cyclin-dependent kinase 2, kinase CK2 holoenzyme, tumour necrosis factor-alpha and screened the prepared Drug Bank library with 1,55,888 compounds against all using three Glide-based docking algorithms namely HTVS, standard precision and extra precise with a docking score ranging from -5.422 to -8.432 Kcal/mol. The poses were filtered with the MM\GBSA calculations, which helped to identify Imidazolidinyl urea C11H16N8O8 (DB14075) as a multitargeted inhibitor for lung cancer, validated with advanced computations like ADMET, interaction pattern fingerprints, and optimised the compound with Jaguar, producing satisfied relative energy. All five complexes were performed with MD Simulation for 100 ns with NPT ensemble class, producing cumulative deviation and fluctuations < 2 Å and a web of intermolecular interaction, making the complexes stable. Further, the in-vitro analysis for morphological imaging, Annexin V/PI FACS assay, ROS and MMP analysis caspase3//7 activity were performed on the A549 cell line producing promising results and can be an option to treat lung cancer at a significantly cheaper state.Communicated by Ramaswamy H. Sarma.

A Review on Picrosides Targeting NFκB and its Proteins for Treatment of Breast Cancer.

Breast cancer is the most frequently diagnosed disease causing most deaths in women worldwide. Chemotherapy and neo-adjuvant therapy are the standard method of treatment in early stages of breast cancer. However drug resistance in breast cancer limit the use of these methods for treatment. Research focus is now shifted towards identifying natural phytochemicals with lower toxicity. This review illustrates the NF κB interaction with different signaling pathways in normal condition, breast cancer and other cancer and thus represent a potential target for treatment. No reports are available on the action of picrosides on NFκB and its associated proteins for anticancer activity. In the present review, potential interaction of picrosides with NF-κB and its associated proteins is reviewed for anticancer action. Further, an important facet of this review entails the ADMET analysis of Picroside, elucidating key ADMET properties which serves to underscore the crucial characteristics of Picroside as a potential drug for treating breast cancer. Furthermore, in silico analysis of Picrosides was executed in order to get potential binding modes between ligand (Picrosides II) and NFκB.

Reporting multitargeted potency of Tiaprofenic acid against lung cancer: Molecular fingerprinting, MD simulation, and MTT-based cell viability assay studies.

Lung Cancer (LC) is among the most death-causing cancers, has caused the most destruction and is a gender-neutral cancer, and WHO has kept this cancer on its priority list to find the cure. We have used high-throughput virtual screening, standard precision docking, and extra precise docking for extensive screening of Drug Bank compounds, and the uniqueness of this study is that it considers multiple protein targets of prognosis and metastasis of LC. The docking and MM\GBSA calculation scores for the Tiaprofenic acid (DB01600) against all ten proteins range from -8.422 to -5.727 kcal/mol and - 47.43 to -25.72 kcal/mol, respectively. Also, molecular fingerprinting helped us to understand the interaction pattern of Tiaprofenic acid among all the proteins. Further, we extended our analysis to the molecular dynamic simulation in a neutralised SPC water medium for 100 ns. We analysed the root mean square deviation, fluctuations, and simulative interactions among the protein, ligand, water molecules, and protein-ligand complexes. Most complexes have shown a deviation of <2 Å as cumulative understanding. Also, the fluctuations were lesser, and only a few residues showed the fluctuation with a huge web of interaction between the protein and ligand, providing an edge that supports that the protein and ligand complexes were stable. In the MTT-based Cell Viability Assay, Tiaprofenic Acid exhibited concentration-dependent anti-cancer efficacy against A549 lung cancer cells, significantly reducing viability at 100 µg/mL. These findings highlight its potential as a therapeutic candidate, urging further exploration into the underlying molecular mechanisms for lung cancer treatment.

CropGCNN: color space-based crop disease classification using group convolutional neural network.

Classifying images is one of the most important tasks in computer vision. Recently, the best performance for image classification tasks has been shown by networks that are both deep and well-connected. These days, most datasets are made up of a fixed number of color images. The input images are taken in red green blue (RGB) format and classified without any changes being made to the original. It is observed that color spaces (basically changing original RGB images) have a major impact on classification accuracy, and we delve into the significance of color spaces. Moreover, datasets with a highly variable number of classes, such as the PlantVillage dataset utilizing a model that incorporates numerous color spaces inside the same model, achieve great levels of accuracy, and different classes of images are better represented in different color spaces. Furthermore, we demonstrate that this type of model, in which the input is preprocessed into many color spaces simultaneously, requires significantly fewer parameters to achieve high accuracy for classification. The proposed model basically takes an RGB image as input, turns it into seven separate color spaces at once, and then feeds each of those color spaces into its own Convolutional Neural Network (CNN) model. To lessen the load on the computer and the number of hyperparameters needed, we employ group convolutional layers in the proposed CNN model. We achieve substantial gains over the present state-of-the-art methods for the classification of crop disease.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis.

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\GBSA algorithms. Imidurea has shown docking and MM\GBSA scores of -8.21 to -4.75 Kcal/mol and -64.16 to -29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

Microbial Biofilm Inhibition Using Magnetic Cross-Linked Polyphenol Oxidase Aggregates.

Microbial biofilm accumulation poses a serious threat to the environment, presents significant challenges to different industries, and exhibits a large impact on public health. Since there has not been a conclusive answer found despite various efforts, the potential green and economical methods are being focused on, particularly the innovative approaches that employ biochemical agents. In the present study, we propose a bio-nanotechnological method using magnetic cross-linked polyphenol oxidase aggregates (PPO m-CLEA) for inhibition of microbial biofilm including multidrug resistant bacteria. Free PPO solution showed only 55-60% biofilm inhibition, whereas m-CLEA showed 70-75% inhibition, as confirmed through microscopic techniques. The carbohydrate and protein contents in biofilm extracellular polymeric substances (EPSs) were reduced significantly. The m-CLEA demonstrated reusability up to 5 cycles with consistent efficiency in biofilm inhibition. Computational work was also done where molecular docking of PPO with microbial proteins associated with biofilm formation was conducted, resulting in favorable binding scores and inter-residual interactions. Overall, both in vitro and in silico results suggest that PPO interferes with microbial cell attachment and EPS formation, thereby preventing biofilm colonization.

BatTS: a hybrid method for optimizing deep feedforward neural network.

Deep feedforward neural networks (DFNNs) have attained remarkable success in almost every computational task. However, the selection of DFNN architecture is still based on handcraft or hit-and-trial methods. Therefore, an essential factor regarding DFNN is about designing its architecture. Unfortunately, creating architecture for DFNN is a very laborious and time-consuming task for performing state-of-art work. This article proposes a new hybrid methodology (BatTS) to optimize the DFNN architecture based on its performance. BatTS is a result of integrating the Bat algorithm, Tabu search (TS), and Gradient descent with a momentum backpropagation training algorithm (GDM). The main features of the BatTS are the following: a dynamic process of finding new architecture based on Bat, the skill to escape from local minima, and fast convergence in evaluating new architectures based on the Tabu search feature. The performance of BatTS is compared with the Tabu search based approach and random trials. The process goes through an empirical evaluation of four different benchmark datasets and shows that the proposed hybrid methodology has improved performance over existing techniques which are mainly random trials.

In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer.

The cervix is the lowermost part of the uterus that connects to the vagina, and cervical cancer is a malignant cervix tumour. One of this cancer's most important risk factors is HPV infection. In the approach to finding an effective treatment for this disease, various works have been done around genomics and drug discovery. Finding the major altered genes was one of the most significant studies completed in the field of cervical cancer by TCGA (The Cancer Genome Atlas), and these genes are TGFBR2, MED1, ERBB3, CASP8, and HLA-A. The greatest genomic alterations were found in the PI3K/MAPK and TGF-Beta signalling pathways, suggesting that numerous therapeutic targets may come from these pathways in the future. We, therefore, conducted a combined enrichment analysis of genes gathered from various works of literature for this study. The final six key genes from the list were obtained after enrichment analysis using GO, KEGG, and Reactome methods. The six proteins against the identified genes were then subjected to a docking-based screening against a library of 6,87,843 prepared natural compounds from the ZINC15 database. The most stable compound was subsequently discovered through virtual screening to be the natural substance Quinic acid, which also had the highest binding affinity for all six proteins and a better docking score. To examine their stability, the study was extended to MM/GBSA and MD simulations on the six docked proteins, and comparative docking-based calculations led us to identify the Quinic Acid as a multitargeted compound. The overall deviation of the compound was less than 2 Å for all the complexes considered best for the biological molecules, and the simulation interaction analysis reveals a huge web of interaction during the simulation.Communicated by Ramaswamy H. Sarma.

Structural-functional analysis and molecular characterization of arsenate reductase from Enterobacter cloacae RSC3 for arsenic biotransformation.

Enterobacter cloacae RSC3 isolated from an industrial pesticide site transformed arsenate into arsenite. The arsenate is transported by membrane-bound phosphate transporter and transformed to arsenite by arsenate reductase (arsC). E. cloacae RSC3 produced an arsenate reductase enzyme with a maximum activity of 354 U after 72 h of incubation. Arsenate reductase was found to be active and stable at a wide range of temperatures (20 and 45 °C) and pH (5-10), with maximum activity at 35 °C and pH 7.0. The arsenate reductase protein was further characterised molecularly using different bioinformatics tools. The 3D structure of ArsC protein was predicted by homology modelling and validated by the Ramachandran plot with 91.9% residues in the most favoured region. ArsC protein of E. cloacae RSC3 revealed structural homology with ArsC from PDB ID: 1S3C. The gene ontology results also showed that the ArsC protein had a molecular functionality of the arsenate reductase (glutaredoxin) activity and the biological function of cellular response to DNA damage stimulus. Molecular docking analysis of 3D structures using AutoDock vina-1.5.7 server predicted four ligand binding active site residues at Gln70, Asp68, Leu68, and Leu63. Strong ArsC-arsenate ion interaction was observed with binding energy -1.03 kcal/mol, indicating significant arsenate reductase activity and specificity of ArsC protein. On the basis of molecular dynamics simulation analysis, the RMSD and RMSF values revealed the stability of ArsC protein from E. cloacae RSC3. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03730-9.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study.

BACKGROUND: AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools. METHODS: Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases. RESULTS: In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer. CONCLUSIONS: This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Computational screening and MM/GBSA-based MD simulation studies reveal the high binding potential of FDA-approved drugs against Cutibacterium acnes sialidase.

Cutibacterium acnes is an opportunistic pathogen linked with acne vulgaris, affecting 80-90% of teenagers globally. On the leukocyte (WBCs) cell surface, the cell wall anchored sialidase in C. acnes virulence factor, catalysing the sialoconjugates into sialic acids and nutrients for C. acnes resulting in human skin inflammation. The clinical use of antibiotics for acne treatments has severe adverse effects, including microbial dysbiosis and resistance. Therefore, identifying inhibitors for primary virulence factors (Sialidase) was done using molecular docking of 1030 FDA-approved drugs. Initially, based on binding energies (ΔG), Naloxone (ZINC000000389747), Fenoldopam (ZINC000022116608), Labetalol (ZINC000000403010) and Thalitone (ZINC000000057255) were identified that showed high binding energies as -10.2, -10.1, -9.9 and -9.8 kcal/mol, respectively. In 2D analysis, these drugs also showed considerable structural conformer of hydrogen and hydrophobic interactions. Further, a 100 ns MD simulation study found the lowest deviation and fluctuations with various intermolecular interactions to stabilise the complexes. Out of 4, the Naloxone molecule showed robust, steady, and stable RMSD 0.23 ± 0.18 nm. Further, MMGBSA analysis supports MD results and found strong binding energy (ΔG) -29.71 ± 4.97 kcal/mol. In Comparative studies with Neu5Ac2en (native substrate) revealed naloxone has a higher affinity for sialidase. The PCA analysis showed that Naloxone and Thalitone were actively located on the active site, and other compounds were flickered. Our extensive computational and statistical report demonstrates that these FDA drugs can be validated as potential sialidase inhibitors.Communicated by Ramaswamy H. Sarma.

Cyclooxygenase-2 as a therapeutic target against human breast cancer: A comprehensive review.

Cyclooxygenase-2 (COX-2) is a key aspect of the physiology and pathogenesis of various cancer types. Overexpression of this enzyme is responsible for the elevated prostaglandin production and characteristic feature of breast cancer. Inhibition of COX-2 derived prostanoids facilitates anti-inflammatory, analgesic, and antipyretic effects of non-steroid anti-inflammation drugs. The overexpression of COX-2 is associated with inflammation, pain, and fever. The present study provides the updated relevant literature describing the role of well-characterized isoforms of cyclooxygenase with particular emphasis on COX-2, mechanism of action, the effect of the drug, combinatorial drugs, and microarray-based differential expression analysis and network analysis. We have discussed the currently used combinatorial treatments and their challenges in breast cancer. This article is categorized under: Cancer > Computational Models Cancer > Molecular and Cellular Physiology.

Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronaviridae family, causing major destructions to human life directly and indirectly to the economic crisis around the world. Although there is significant reporting on the whole genome sequences and updated data for the different receptors are widely analyzed and screened to find a proper medication. Only a few bioassay experiments were completed against SARS-CoV-2 spike protein. We collected the compounds dataset from the PubChem Bioassay database having 1786 compounds and split it into the ratio of 80-20% for model training and testing purposes, respectively. Initially, we have created 11 models and validated them using a fivefold validation strategy. The hybrid consensus model shows a predictive accuracy of 95.5% for training and 94% for the test dataset. The model was applied to screen a virtual chemical library of Natural products of 2598 compounds. Our consensus model has successfully identified 75 compounds with an accuracy range of 70-100% as active compounds against SARS-CoV-2 RBD protein. The output of ML data (75 compounds) was taken for the molecular docking and dynamics simulation studies. In the complete analysis, the Epirubicin and Daunorubicin have shown the docking score of -9.937 and -9.812, respectively, and performed well in the molecular dynamics simulation studies. Also, Pirarubicin, an analogue of anthracycline, has widely been used due to its lower cardiotoxicity. It shows the docking score of -9.658, which also performed well during the complete analysis. Hence, after the following comprehensive pipeline-based study, these drugs can be further tested in vivo for further human utilization.Communicated by Ramaswamy H. Sarma.