Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
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Fenugreek (Trigonella foenum-graecum) is used as a spice throughout the world. It is known for its medicinal properties such as\nantidiabetic, anticarcinogenic, and immunological activities. The present study shows the properties and the nutritional quality\nof fenugreek seed extract and focuses on screening of active compounds in drug designing for type 2 diabetes and breast cancer.\nQuantitative analysis was used to calculate the percentages of protein, carbohydrates moisture, fatty acid, galactomannan, oil, and\namino acid. Phytochemical analysis revealed the presence of flavonoids, terpenoids, phenols, proteins, saponins, and tannins in\nfenugreek seed extracts. Molecular docking and molecular dynamics simulation-based computational drug discovery methods\nwere employed to address the role of fenugreek seed constituents against type 2 diabetes and breast cancer. The computational\nresults reveal that the compound galactomannan can be ascribed as potential drug candidate against breast cancer and type 2\ndiabetes rendered by higher molecular dock scores, stable molecular dynamics (MD) simulations results, and lower binding energy\ncalculations....
Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to\nestablish mathematical relationships between molecular structures and their biological activities. In the\npresent article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine\n(TIBO) derivatives using different classifiers, such as support vector machines, artificial neural\nnetworks, random forests, and decision trees. The goal is to propose classification models that\nwill be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1\nreverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the\nvalidity of the established models, all of them were subjected to various validation tests: internal\nvalidation, Y-randomization, and external validation. The established classification models have\nbeen successful. The correct classification rates reached 100% and 90% in the learning and test\nsets, respectively. Finally, molecular docking analysis was carried out to understand the interactions\nbetween reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen\nbond interactions led to the identification of active binding sites. The established models could help\nscientists to predict the inhibition activity of untested compounds or of novel molecules prior to\ntheir synthesis. Therefore, they could reduce the trial and error process in the design of human\nimmunodeficiency virus (HIV) inhibitors....
The hepatitis E virus- (HEV-) helicase as a novel drug-target was evaluated. While cell culture model was used for mutational\ncharacterization of helicase, in silico protein modeling and virtual screening were employed to identify helicase inhibitors. None of\nthe saturation mutant replicons significantly affected RNA replication. Notably,mutants encompassing the Walker motifs replicated\nas wild-type, showing indispensability of nucleotides conservation in viability compared to known criticality of amino acids. A\n3D modeling of HEV-helicase and screening of a compound dataset identified ten most promising inhibitors with drug likeness,\nnotably, JFD02650, RDR03130, and HTS11136 that interacted with Walker A residues Gly975, Gly978, Ser979, and Gly980. Our\nmodel building and virtual identification of novel helicase inhibitors warrant further studies towards developing anti-HEV drugs....
Molecular modeling by means of docking and molecular dynamics (MD) has become an\nintegral part of early drug discovery projects, enabling the screening and enrichment of large libraries\nof small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based\nmolecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically\nwith the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique,\nwhich results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to\ntheir targets. Herein, we discuss the role and contribution of docking and MD to the development and\noptimization of new nucleic acid-based molecules. This review focuses on the different approaches\ncurrently available for molecular modeling applied to NA interaction with proteins. We discuss\ntopics ranging from structure prediction to docking and MD, highlighting their main advantages and\nlimitations and the influence of flexibility on their calculations....
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