Search

• Synthesis of Cu(II), Co(II) and Ni(II) complexes of novel Schiff base ligand. • Computational calculations for the proposed structures were conducted using DFT. • The ligand and Co(II) complex are promising for developing NLO materials. • Molecular docking reveals a high binding affinity of ligand and Co(II) complex. • Cumulative deviation and fluctuation were below 2Å with many intermolecular interactions. Novel Schiff base metal complexes diaqua(4-chloro-2-{[(4-(dimethylamino)phenyl)methylene]amino}phenolato)M(II) ion, (M = copper/cobalt/nickel) have been synthesized using the ligand 4-chloro-2-((4-(dimethylamino)benzylidene)amino)phenol, which was prepared by reacting 2-amino-4-chlorophenol with 4-(dimethylamino)benzaldehyde in a molar ratio of 1:1. The synthesized compounds were characterized based on elemental analysis, FTIR, UV–vis., 1H and 13C NMR, powder XRD, mass spectrometry and magnetic studies. Spectroscopic investigations demonstrated that the metal atom coordinates to a bidentate Schiff base ligand via the azomethine nitrogen and phenolic oxygen. The appropriate molecular geometry for each metal complex has been proposed through the analysis of spectroscopic and analytical data, Specifically, tetrahedral geometry has been proposed for Co(II), Ni(II), and square planar geometry for Cu(II) complex. Theoretical calculations employing Density Functional Theory (DFT) validated the experimental results, elucidating optimized geometries, frontier molecular orbitals, natural bond orbital distributions, molecular electrostatic potentials, non-linear optical properties, IR vibrational modes, and UV absorbance profiles. Additionally, the compounds' biological efficacy was validated through molecular docking against receptors for Gram(+ve) bacteria, Bacillus subtilis (PDB ID: 5H67), Staphylococcus aureus (PDB ID: 3TY7) and Gram(-ve) bacteria, Escherichia coli (PDB ID: 3T88), Proteus vulgaris (PDB ID: 5I39). The outcome revealed that Co(II) complex exhibited highest binding energy with Escherichia coli and Bacillus subtilis while the ligand with Proteus vulgaris (5I39) and Staphylococcus aureus (3TY7). Further the study was extended to the MD Simulation in water for 100 ns to evaluate the binding affinities and its stabilities by analyzing the deviation, fluctuations, and intermolecular interactions. The research presented here showcase the potential of employing a Schiff base ligand and its Co(II) complex in the synthesis of novel, highly potent antibacterial agents to combat emerging diseases, which holds considerable importance in the field of pharmaceutical science—however, in-vitro studies are needed to validate the results. [Display omitted] [ABSTRACT FROM AUTHOR]

• Synthesis, experimental spectral characterization of synthesized compounds. • Evaluation of antifungal activity, DNA binding. • The molecular modeling study also carried out of lead compounds. In this study, eleven new 2-hydrazinobenzothizole derivatives (Z1–Z11) were synthesized by condensation reaction. The molecular structure of the derivatives was confirmed using FT-IR, NMR, and mass spectrometry. The heterocyclic derivatives (Z1–Z11) were tested for their in vitro antifungal activity against the fungal strains C. albicans, C. glabrata , and C. tropicalis. The outcomes showed that heterocyclic analog Z5 , with a MIC value of 450 μM , demonstrates noteworthy activity against strain C. tropicalis , while Z7 displays antifungal activity against C. tropicalis , with MIC values of 480 μM. When compared to the common medication fluconazole. Ct-DNA binding studies of both lead compounds were carried out using UV–visible, fluorescence, and cyclic voltammetry (CV) measurements. Results from the binding study depicted that the compounds demonstrated a groove mode of binding. In addition, molecular docking analysis of the ligand molecules with PDBID: 5FSA revealed numerous interactions, strong binding, and a high MM/GBSA score, indicating a stable complex. Furthermore, a 100ns molecular dynamics (MD) simulation demonstrated minimal deviation and fluctuations, along with significant intermolecular interactions, further confirming the stability of the complex. [Display omitted] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present work describes the in vitro synthesis and characterization of Zinc oxide nanoparticles (ZnO NPs) using an enzyme alpha amylase, the synthesized nanoparticles were used to study their beneficial effect in the growth and development of Brassica juncea. Transmission Electron Microscope (TEM) image reveals the average size of ZnO NPs was 11 nm and X-ray powder diffraction (XRD) suggests nanoparticles were crystalline in nature. In-silico study confirmed lysine, glutamine and tyrosine present in alpha amylase enzyme, plays a crucial role in the reduction of Zinc acetate dihydrate to ZnO NPs. The biochemical parameters and oxidative enzymes of Brassica juncea were compared with ZnO NPs treated plants. The effect of ZnO NPs on the cellular expression of metal tolerant protein (BjMTP) and cation efflux transporter gene (BjCET2) was also studied. The results indicate that nanoparticles can be used as a replacement for traditional harmful chemical fertilizers. [ABSTRACT FROM AUTHOR]

Abstract Plant-insect interaction system is a widely studied model of the ecosystem. Numerical understanding of this counter system has developed from initial analogy based approach with a predator-prey model to its recent mathematical interpretation including plant immunity concept. In current work, we propose an extension to this model, including molecular interactions behind the plant defense system and its effect on ecological behaviour. Inspired from biomolecular interaction given by Louis and Shah in 2014, we propose here a mathematical model to depict molecular dependence and control of plant insect interaction system. Insect infestation mediated Botrytis Induced Kinase-1 (BIK1) induction resulted in inhibition of Phyto Alexin Deficient-4 (PAD4) protein. Lowered PAD4 triggers the plant defense mechanism, leading to degraded plant immune potential and thereby reducing the plant quality. We mathematically adapt these interactions to show their influence on plant-insect interaction system and hypothesize the significance of BIK1 inhibition leading to the improved plant quality. We implemented the plethora of computational modeling and all atom MD simulations to explain the Plant-Insect- PAD4-BIK1 interaction network and identify potential molecular mechanisms of plant improvement by BIK1 inhibition. [ABSTRACT FROM AUTHOR]

• The spectroscopic and X-ray single crystal studies of pyrazoline derivative. • Antifungal activity was evaluated and further studied by molecular dynamics. • FMO, UV–Vis, NBO, NHO, NLO, MEP and hirshfeld surface analysis for charge transfer studies. In this work, analog 3b (5-(4-(dimethylamino) phenyl)-3-(4-methoxyphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide) of pyrazoline derivative was produced based on the active site (CYP51) analysis. To elucidate its molecular structure, various techniques including FT-IR, UV–visible, NMR, mass spectrometry and X-ray crystallography were utilised. The bond lengths and bond angles occur in monoclinic with P21/n space group for 3b, and z = 8 corresponds to compound 3b, and the lattice parameters show, a = 14.5480(14) Å, b = 17.0119(17) Å, c = 14.6257(14) Å, α = 90˚, β = 98.646(3)˚, γ = 90˚ per unit cell. Analog 3b also showed significant antifungal activity against candida strains, C. albicans, C. glabrata , and C. tropicalis and molecular docking performed at the active site of 14-alpha demethylase, and an estimated ADME assay was calculated. In addition, simulation of the CYP51 protein-ligand complexes at a time scale of 100 ns showed that the mean RMSD is 0.31 nm. According to the simulation and MMGBSA data, protein-ligand complexes are stable and exhibit stable interactions up to 100 ns of simulation time. The DNA binding was revealed by using UV–vis, fluorescence and cyclic voltammetry measurements. TD-DFT calculation was used to study several computational structural characteristics of molecule. We also used Hirshfeld surface analysis (HSA), FTIR, UV–Vis. and NMR spectra to compare the structural features of theoretical and experimental findings of the molecule 3b. The comprehensive in-silico and experimental study confirm the synthesised pyrazoline analog's antifungal, antioxidant, and DNA binding properties. [Display omitted] [ABSTRACT FROM AUTHOR]

Ebola virus (EBOV), a member of Filoviridae virus family under the genus Ebolavirus, has emerged as a dangerous and potential threat to human health globally. It causes a severe and deadly hemorrhagic fever in humans and other mammals, called Ebola Virus Disease (EVD). In recent outbreaks of EVD, there has been loss of large numbers of individual’s life. Therefore, EBOV has attracted researchers and increased interests in developing new models for virus evolution, and therapies. The EBOV interacts with the immune system of the host which led to understand how the virus functions and effects immune system behaviour. This article presents an exhaustive review on Ebola research which includes EVD illness, symptoms, transmission patterns, patho-physiology conditions, development of antiviral agents and vaccines, resilient health system, dynamics and mathematical model of EBOV, challenges and prospects for future studies. [ABSTRACT FROM AUTHOR]

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 (Δ G ) -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.

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., Competing Interests: Khalid Raza is an Academic Editor for PeerJ Computer Science., (© 2024 Ahmad et al.)

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.

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., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.

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 C 11 H 16 N 8 O 8 (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.

Background: Epidermal growth factor receptor (EGFR/HER-1) and its role in tumor development and progression through the mechanism of tumor angiogenesis is prevalent in non-small lung cancer, head and neck cancer, cholangiocarcinoma & glioblastoma. Previous treatments targeting the oncogenic activity of EGFR's kinase domain have been hindered by acquired mutational resistance and side effects from existing drugs like erlotinib, highlighting the need for new EGFR inhibitors through structure- based drug designing., Objective: The research aims to develop novel quinazoline derivatives through structure-based virtual screening, molecular docking, and molecular dynamics simulation to potentially interact with EGFR's kinase domain and impede tumor angiogenic phenomenon., Methods: Quinazoline derivatives were retrieved and filtered from the PubChem database using structure- based virtual screening and the Lipinski rule of five drug-likeness studies. Molecular docking-based virtual screening methods and molecular dynamics simulation were then carried out to identify top leads., Results: A total of 1000 quinazoline derivatives were retrieved, with 671 compounds possessing druglike properties after applying Lipinski filters. Further filtration using ADME and toxicity filters yielded 28 compounds with good pharmacokinetic profiles. Docking-based virtual screening identified seven compounds with better binding scores than the control drug, dacomitinib. After cross-checking binding scores, three top compounds QU524, QU571, and QU297 were selected for molecular dynamics simulation study of 100 ns interval using Desmond module of Schrodinger maestro to understand their conformational stability., Conclusion: The research results showed that the selected quinazoline leads exhibited better binding affinity and conformational stability than the control drug, erlotinib. These compounds also had good pharmacokinetic and pharmacodynamic profiles and did not violate Lipinski's rule of five limits. The findings suggest that these leads have the potential to target EGFR's kinase domain and inhibit the EGFR-associated phenomenon of tumor angiogenesis., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in Momordica charantia and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from Momordica charantia using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of M. charantia . The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, in vitro and in vivo studies of the ligand against the diabetic enzymes is recommended.Communicated by Ramaswamy H. Sarma.

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.

The POTE family comprises 14 paralogues and is primarily expressed in Prostrate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins' structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study's outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer., (© 2021 Nisar Wani et al., published by De Gruyter, Berlin/Boston.)

Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog., (© 2021 Sahar Qazi et al., published by De Gruyter, Berlin/Boston.)

Escherichia coli is a harmful Gram-negative bacterium commonly found in the gut of warm-blooded organisms and affects millions of people annually worldwide. In this study, we have synthesized a ZnO-CuO nanocomposite (NC) by a co-precipitation method and characterized the as-synthesized NC using FTIR spectroscopy, XRD, Raman spectroscopy, and FESEM techniques. To fabricate the immunosensor, the ZnO-CuO NC composite was screen-printed on gold-plated electrodes followed by physisorption of the anti-LPS E. coli antibody. The biosensor was optimized for higher specificity and sensitivity. The immunosensor exhibited a high sensitivity (11.04 μA CFU mL -1 ) with a low detection limit of 2 CFU mL -1 with a redox couple. The improved performance of the immunosensor is attributed to the synergistic effect of the NC and the antilipopolysaccharide antibody against E. coli . The selectivity studies were also carried out with Staphylococcus aureus to assess the specificity of the immunosensor. Testing in milk samples was done by spiking the milk samples with different concentrations of E. coli to check the potential of this immunosensor. We further checked the affinity between ZnO-CuO NC with E. coli LPS and the anti-LPS antibody using molecular docking studies. Atomic charge computation and interaction analyses were performed to support our hypothesis. Our results discern that there is a strong correlation between molecular docking studies and electrochemical characterization. The interaction analysis further displays the strong affinity between the antibody-LPS complex when immobilized with a nanoparticle composite (ZnO-CuO)., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

The novel coronavirus (2019-nCoV) has led to the apex pandemic in 2020, responsible for the recent sequential spread. The 2019-nCoV has been discerned to be a Beta-BAT-SARS-CoV-2 lineage. The gene ontology (GO) identifies the virus to be localized in the Golgi apparatus with a vital molecular function of binding and viral progression. The source organism is almost all bats, further suggesting that the host of this virus is bat rather than civets or snakes, and has motifs which are perfect matches to various human and mouse genomic motifs such as- zinc fingers , DNA-binding domains, and basic helix-loop-helix factors. It has basic clusters of orthologs (COGs)- Superfamily I DNA and RNA helicases and helicase subunits and Predicted phosphatase homologous to the C-terminal domain of histone macroH2A1 respectively hinting at the epigenetic alterations which could be the reason behind the "novelty" the virus. Our study discerns that the SARS-CoV-2 endorses the epigenetic mechanism essential for its replication and reproduction in the host organism. Furthermore, we identified six non-toxic disinfectants with higher pharmacokinetics and pharmacodynamics properties, namely Quaternary Ammonium , Octanoic acid , Citric acid , Phenolics , 1,2-Hexanediol , and Thymol , that bind to lyases, nuclear receptors, fatty acids binding family, enzymes, and family AG protein-coupled receptors indicating that they target the nucleocapsid (N) protein, envelope (E) protein, membranous proteins of the novel coronavirus, thus, killing it from the surfaces when sprayed and are not harmful to the biological environment., Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-021-00655-w., Competing Interests: Conflict of interestAuthors declare that there is no any conflict of interest in the publication of this manuscript., (© Indian Virological Society 2021.)

Ovarian cancer is one of the highly prominent gynecological malignancies after breast cancer. Although myriad literature is available, there is no specific biomarker available for the personalized treatment strategy. The unavailability of effective drug therapy for ovarian cancer calls for an urgent push in its development from the multidisciplinary scientific community. Indian Ayurvedic medicine pharmacology is widely appreciated and accepted for its immense healthcare benefits. Bioinformatics and cheminformatics approaches can be effectively used to screen phytochemicals present in the Indian Ayurvedic plants against ovarian cancer target receptors. Recent studies discern that POTE, a cancer-testis antigen (CTA) family, plays a crucial role in the proliferation and progression of cancers including ovarian cancer. Specifically, POTEE paralog has been observed to be hypermethylated in ovarian cancer. This study undertakes an in silico analysis of Indian Ayurvedic plants for their anticancer efficacy against ovarian cancer proliferation target receptor POTEE. Structures of 100 phytochemicals from 11 Ayurvedic plants were screened with ADME criteria, and qualified phytochemicals were subjected to molecular docking and interaction analysis. Only 6 phytochemicals having a high affinity to the target receptor (POTEE) were then subjected to an all-atom replica exchange molecular dynamics simulation for 50 ns. Binding affinities of 6 phytochemicals cedeodarin, deodarin, hematoxylin, matairesinol, quercetin, and taxifolin with POTEE were -8.1, -7.7, -7.7, -7.9, -8.0, and - 7.7 kcal/mol, respectively, and their RMSD were recorded as zero. This study concludes that phytochemicals present in Indian Ayurvedic plants namely Cedrus deodara and Asparagus racemosus possess inhibitory effects against ovarian cancer proliferation receptor POTEE.

The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression-based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI., (Copyright © 2018 Elsevier B.V. All rights reserved.)