Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches.

Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen's survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub.

Synthesis and characterization of biologically active flurbiprofen amide derivatives as selective prostaglandin-endoperoxide synthase II inhibitors: In vivo anti-inflammatory activity and molecular docking.

A novel series of twenty two flurbiprofen amides (1-22) were designed and synthesized in good to excellent yields by reacting flurbiprofen acid with various aromatic/aliphatic primary amines in the presence of 1,1­carbonyldiimidazole (CDI) in basic medium using acetonitrile as solvent. Structures of the synthesized derivatives were elucidated with the help of HR-ESI-MS, 1H-, and 13C NMR spectroscopy and finally screened them for their in-vivo anti-inflammatory potential using carrageenan induced mice paw oedema assay. Among the series, four compounds (8, 14, 15, and 20) displayed excellent activity ranging from 59.0 to 77.7 % decrease, while eight compounds (1, 3, 7, 10, 12, 13, 17, and 18) exhibited good activity in the decrease range of 37.0-50.0 %. Additionally, four compounds (2, 6, 16, and 22) attributed less activity, while the remaining six compounds (4, 5, 9, 11, 19, and 21) were found to be inactive. Furthermore, the In-silico studies were executed on the synthesized derivatives in order to explain the binding interface of compounds with the active sites of prostaglandin endoperoxide-synthase II enzyme.