MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Network pharmacology-based evaluation of natural compounds with paclitaxel for the treatment of metastatic breast cancer.

Metastatic breast cancer is a prevalent life-threatening disease. Paclitaxel (PTX) is widely used in metastatic breast cancer therapy, but the side effects limit its chemotherapeutic application. Multidrug strategies have recently been used to maximize potency and decrease the toxicity of a particular drug by reducing its dosage. Therefore, we have evaluated the combined anti-cancerous effect of PTX with tested natural compounds (andrographolide (AND), silibinin (SIL), mimosine (MIM) and trans-anethole (TA)) using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, trypan blue dye exclusion assay, proliferating cell nuclear antigen (PCNA) staining, network pharmacology, molecular docking, molecular dynamics (MD) and in vivo chick chorioallantoic membrane (CAM) angiogenesis assay. We observed a reduction in the IC50 value of PTX with tested natural compounds. Further, the network pharmacology-based analysis of compound-disease-target (C-D-T) network showed that PTX, AND, SIL, MIM and TA targeted 55, 61, 56, 31 and 18 proteins of metastatic breast cancer, respectively. Molecular docking results indicated that AND and SIL inhibited the C-D-T network's core target kinase insert domain receptor (KDR) protein more effectively than others. While MD showed that the binding of AND with KDR was stronger and more stable than others. In trypan blue dye exclusion assay and PCNA staining, AND and SIL along with PTX were found to be more effective than PTX alone. CAM assay results suggested that AND, SIL and TA increase the anti-angiogenic potential of PTX. Thus, natural compounds can be used to improve the anti-cancer potential of PTX.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

A multiparametric organ toxicity predictor for drug discovery.

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

Cardiotonic steroids as potential Na+/K+-ATPase inhibitors - a computational study.

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na+/K+-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na+/K+-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na+/K+-ATPase-ouabain and Na+/K+-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.

Ameliorative effects of curcumin towards cyclosporine-induced genotoxic potential: an in vitro and in silico study.

Several studies documented the ameliorative effects of curcumin which plays a pivotal role in radical scavenging activities. It also participates in various cellular pathways and interacts with multiple targets. In the present study, we investigated the ameliorative effect of curcumin upon chromosomal genotoxicity induced by cyclosporine, an immunosuppressant, using in vitro approaches. A plausible mechanism of how curcumin mitigates the genotoxic implications of cyclosporine was ascertained using in silico tools. We observed that the curcumin reduces the genotoxic consequences made by cyclosporine upon cell cycle checkpoints and associated chromosomal/DNA manifestations. In addition, we presented the mechanistic details of curcumin interaction with various biomacromolecule types using docking experiments which showed that the possible radical scavenging activities can only be emerged by inducing the expression of antioxidant enzymes, supported by available experimental evidences. We anticipate that the induction of antioxidant enzymes by curcumin would activate Nrf2-Keap1 pathway as the plausible mechanism to exert anti-inflammatory response as demonstrated in renal epithelial cells.

MDCKpred: a web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features.

Structure-based models to understand the transport of small molecules through biological membrane can be developed by enumerating intermolecular interactions of the small molecule with a biological membrane, usually a dimyristoylphosphatidylcholine (DMPC) monolayer. This ADME (absorption, distribution, metabolism, and excretion) property based on Madin-Darby Canine Kidney (MDCK) cell line demonstrates intestinal drug absorption of small molecules and correlated to human intestinal absorption which acts as a determining factor to forecast small-molecule prioritization in drug-discovery projects. We present here the development of MDCKpred web-tool which calculates MDCK permeability coefficient of small molecule based on the regression model, developed using membrane-interaction chemical features. The web-tool allows users to calculate the MDCK permeability coefficient (nm/s) instantly by providing simple descriptor inputs. The chemical-interaction features are derived from different parts of the DMPC molecule viz. head, middle, and tail regions and accounts overall intermolecular contacts of the small molecule when passively diffused through the phospholipid-rich biological membrane. The MDCKpred model is both internally (R2 = .76; [Formula: see text]= .68; Rtrain = .87; Rtest = .69) and externally (Rext = .55) validated. Furthermore, we used natural molecules as application examples to demonstrate its utility in lead exploration and optimization projects. The MDCKpred web-tool can be accessed freely at http://www.mdckpred.in . This web-tool is designed to offer an intuitive way of prioritizing small molecules based on calculated MDCK permeabilities.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints.

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

Thiacalix[4]arene-tetra-(quinoline-8- sulfonate): a Sensitive and Selective Fluorescent Sensor for Co (II).

A novel fluorescent thiacalix[4]arene-tetra-(quinoline-8-sulfonate) (TCTQ8S) was synthesized by condensation of thiacalix[4]arene (TCA) and 8-quinoline sulfonyl chloride(8QSC). TCTQ8S was characterized by ESI-MS, (1)H-NMR and (13)C-NMR spectroscopic methods. TCTQ8S was found to be an efficient "turn-off" fluorescent sensor for the selective and sensitive recognition of Co(II) ions. The Job's plot measurement reveals a 1:1 stoichiometric ratio. The designed chemosensor exhibited high selectivity toward Co(II) ions vs. other tested metal ions, with a detection limit of up to 1.038 × 10(-9) M. The binding constant and quantum yield for the complex were also determined. Molecular docking studies have been successfully performed to support 1:1 binding of TCTQ8S with the Co(II) metal ion. TCTQ8S was evaluated for real sample analysis on water sample for the detection of Co(II). Graphical Abstract Thiacalix derivatized fluorescent sensor for the selective detection of Co(II).

Compound prioritization from inverse docking experiment using receptor-centric and ligand-centric methods: a case study on Plasmodium falciparum Fab enzymes.

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor-centric and ligand-centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge-driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative-structure activity relationship and multilayer perceptron models.

Excavating phytochemicals from plants possessing antiviral activities for identifying SARS-CoV hemagglutinin-esterase inhibitors by diligent computational workflow.

Coronaviruses (CoVs) belong to a group of RNA viruses that cause diseases in vertebrates including. Newer and deadlier than SARS CoV-2 are sought to appear in future for which the scientific community must be prepared with the strategies for their control. Spike protein (S-protein) of all the CoVs require angiotensin-converting enzyme2 (ACE2), while CoVs also require hemagglutinin-acetylesterase (HE) glycoprotein receptor to simultaneously interact with O-acetylated sialic acids on host cells, both these interactions enable viral particle to enter host cell leading to its infection. Target inhibition of viral S-protein and HE glycoprotein receptor can lead to a development of therapy against the SARS CoV-2. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask the active site of, HE glycoprotein which would ideally bind to O-acetylated sialic acids on human host cells. Such molecules can be addressed as 'HE glycoprotein blockers'. A library of 110 phytochemicals from Withania somnifera, Asparagus racemosus, Zinziber officinalis, Allium sativum, Curcuma longa and Adhatoda vasica was constructed and was used under present study. In silico analysis was employed with plant-derived phytochemicals. The molecular docking, molecular dynamics simulations over the scale of 1000 ns (1 µs) and ADMET prediction revealed that the Withania somnifera (ashwagandha) and Asparagus racemosus (shatavari) plants possessed various steroidal saponins and alkaloids which could potentially inhibit the COVID-19 virus and even other CoVs targeted HE glycoprotein receptor.Communicated by Ramaswamy H. Sarma.

Repurposing of anticancer phytochemicals for identifying potential fusion inhibitor for SARS-CoV-2 using molecular docking and molecular dynamics (MD) simulations.

The viral particle, SARS-CoV-2 is responsible for causing the epidemic of Coronavirus disease 2019 (COVID-19). To combat this situation, numerous strategies are being thought for either creating its antidote, vaccine, or agents that can prevent its infection. For enabling research on these strategies, several target proteins are identified where, Spike (S) protein is of great potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for entering the cell. S-protein is a large protein and a portion of it designated as a receptor-binding domain (RBD) is the key region that interacts with ACE2, following to which the viral membrane fuses with the alveolar membrane to enter the human cell. The hypothesis is to identify molecules from the pool of anticancer phytochemicals as a lead possessing the ability to interact and mask the amino acids of RBD, making them unavailable to form associations with ACE2. Such a molecule is termed as 'fusion inhibitor'. We hypothesized to identify fusion inhibitors from the NPACT library of anticancer phytochemicals. For this, all the molecules from the NPACT were screened using molecular docking, the five top hits (Theaflavin, Ginkgetin, Ursolic acid, Silymarin and Spirosolane) were analyzed for essential Pharmacophore features and their ADMET profiles were studied following to which the best two hits were further analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals containing anticancer properties to serve as fusion inhibitors.Communicated by Ramaswamy H. Sarma.

Pinpointing the potential hits for hindering interaction of SARS-CoV-2 S-protein with ACE2 from the pool of antiviral phytochemicals utilizing molecular docking and molecular dynamics (MD) simulations.

SARS-CoV-2, the viral particle, is responsible for triggering the 2019 Coronavirus disease outbreak (COVID-19). To tackle this situation, a number of strategies are being devised to either create an antidote, a vaccine, or agents capable of preventing its infection. To enable research on these strategies, numerous target proteins are identified where Spike (S) protein is presumed to be of immense potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for cell entry. The key region of S-protein that interacts with ACE2 is a portion of it designated as a receptor-binding domain (RBD), following whereby the viral membrane fuses with the alveolar membrane to enter the human cell. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask RBD, rendering them unavailable to form ACE2 interactions. Such a molecule is called the 'S-protein blocker'. A total of 110 phytochemicals from Withania somnifera, Asparagus racemosus, Zinziber officinalis, Allium sativum, Curcuma longa and Adhatoda vasica were used in the study, of which Racemoside A, Ashwagandhanolide, Withanoside VI, Withanoside IV and Racemoside C were identified as top five hits using molecular docking. Further, essential Pharmacophore features and their ADMET profiles of these compounds were studied following to which the best three hits were analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals can serve as S-protein blocker.

Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations.

Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors.

An integrated computational approach to identify GC minor groove binders using various molecular docking scoring functions, dynamics simulations and binding free energy calculations.

Understanding the DNA-ligand interaction mechanism is of utmost importance to design selective inhibitors targeting the GC- and AT-rich DNA. This forms a primary strategy to block the association of transcription factors to promoters and subsequently, reduce the expression of genes. We present here an integrated approach combining various docking scoring functions, selective ligand-based pharmacophore models, molecular dynamics simulations and binding free energy calculations to prioritize natural compounds specific to GC minor groove binding. The approach initially applies a selective ligand-based pharmacophore model built upon known GC minor groove binders to identify potential GC minor groove binders from natural compound repositories. These GC minor groove binders were then cross-examined with selective pharmacophore models (controls) based on AT-rich binders and GC intercalators to assess its unfitness. This approach involves the calculation of binding energies of known GC- and AT minor groove binders using three scoring functions without any constraint on groove specificity of GC- and AT-rich DNA. The evaluation of empirical scoring functions led to enumeration of a new parameter, the energy difference computed using Glide (sensitivity = 80%) to recognize GC-rich binders effectively. Molecular dynamics simulations and binding free energy calculations (MM/GBSA) constituted the final phase of this approach to analyze the interactions of natural molecules (hits) with GC-rich DNA comprehensively. Seven natural molecules were selected which exhibited fewer fluctuations in RMSD and RMSF profiles and better GC-rich DNA binding with low free energies of binding. These natural hits prioritized by this integrated approach can be tested in DNA binding assay.Communicated by Ramaswamy H. Sarma.

Transcriptome-wide miRNA identification of Bacopa monnieri: a cross-kingdom approach.

Bacopa monnieri known as 'Brahmi' is a well-known medicinal plant belonging to Scrophulariaceae family for its nootropic properties. To the best of our knowledge, no characterization data is available on the potential role of micro RNAs (miRNAs) from this plant till date. We present here the first report of computational characterizations of miRNAs from B. monnieri. Owing to the high conservation of miRNAs in nature, new and potential miRNAs can be identified in plants using in silico techniques. Using the plant miRNA sequences present in the miRBase repository, a total of 12 miRNAs were identified from B. monnieri which pertained to 11 miRNA families from the shoot and root transcriptome data. Furthermore, gene ontology analysis of the identified 68 human target genes exhibited significance in various biological processes. These human target genes were associated with signaling pathways like NF-kB and MAPK with TRAF2, CBX1, IL1B, ITGA4 and ITGB1BP1 as the top five hub nodes. This cross-kingdom study provides initial insights about the potential of miRNA-mediated cross-kingdom regulation and unravels the essential target genes of human with implications in numerous human diseases including cancer.

Synthesis, biological evaluation and computational study of novel isoniazid containing 4H-Pyrimido[2,1-b]benzothiazoles derivatives.

Synthesis of novel and potent hit molecules has an eternal demand. It is our continuous study to search novel bioactive hit molecules and as a part of this, a series of novel N'-isonicotinoyl-2-methyl-4-(pyridin-2-yl)-4H-benzo[4,5]thiazolo[3,2-a]pyrimidine-3-carbohydrazide analogs (5a-5n) were synthesized with good yields by the conventional method. The various novel compounds have been characterized and identified by many analytical technique such as IR, 1H NMR, 13C NMR, mass spectral analysis, and elemental analysis. All the synthetic analogs (5a-5n) are evaluated for their in vitro antibacterial and anti-mycobacterial activities against different bacterial strains. Molecular docking and Molecular dynamics studies were helped in revealing the mode of action of these compounds through their interactions with the active site of the Mycobacterium tuberculosis enoyl reductase (InhA) enzyme. The calculated ADMET descriptors for the synthesized compounds validated good pharmacokinetic properties, confirming that these compounds could be used as templates for the development of new Anti-mycobacterial agents.

Ameliorative effects of quercetin against bisphenol A-caused oxidative stress in human erythrocytes: an in vitro and in silico study.

Bisphenol A (BPA) is an endocrine disruptor of xenobiotic type, mainly used for the production of polycarbonate plastic, epoxy resins and non-polymer additives. Because of its wide usages in the environment, the toxic effects of BPA have proved to be harmful to human health. However, its effects on human haemoglobin remain unclear. The affinity between BPA and haemoglobin, as well as erythrocytes, is an important factor in understanding the mechanism of the toxicity of BPA. Flavonoids are strong antioxidants that prevent oxidative stress and Quercetin is a flavonoid found in numerous vegetables and fruits. Therefore, the present investigation was undertaken to investigate whether Quercetin can be used to alleviate the toxic effects of BPA in vitro in human red blood cells (RBC). Venous blood samples were collected from healthy, well-nourished adult volunteers (25-30 years old) by phlebotomy. In a RBC suspension with a cell density of 2 × 104 cell per mL, the concentration of BPA (25-150 µg mL-1) was found to cause an increase in the lipid peroxidation (LPO) and a decrease in the activities of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX) in human RBC. However, the concurrent addition of BPA (150 µg mL-1) and quercetin (10-50 µg mL-1) lead to significant amelioration. In silico studies gave structural insight into BPA and quercetin to decipher the plausible binding mechanism and molecular level recognition.