Pharmacophore-based approach for the identification of prospective UDP-2,3-diacylglucosamine hydrolase (LpxH) inhibitor from natural product database against Salmonella Typhi

Antibiotics are essential for the treatment of typhoid fever, which is caused by Salmonella Typhi. The global increase of antibiotic resistance in S. Typhi is a significant public health threat. The traditional approach to discovering new drugs is a lengthy process. To address this, ligand-based pharmacophore modeling was used to identify potential inhibitors of the S. typhi LpxH protein, a crucial enzyme in the lipid A biosynthesis pathway. A natural compound library of 852,445 molecules was screened against a pharmacophore model developed from known LpxH inhibitors. Further, virtual screening, molecular docking, and MD simulation (100 ns) studies identified two lead compounds, 1615 and 1553. A comparative analysis of both molecules showed that compound 1615 exhibited the highest stability, with the lowest potential energy, minimal fluctuations, and stable hydrogen bonding, indicating strong binding at the active site. Compound 1553 also demonstrated good stability, though with slightly higher fluctuations. Both compounds underwent toxicity prediction and ADMET analysis, showing favorable drug-like properties, with compound 1615 emerging as the most promising inhibitor due to its optimal electronic energy and minimal chemical potential. The study concluded that compounds 1615 and 1553 hold significant potential as lead molecules for developing new treatments against drug-resistant S. Typhi infections. This study illustrates how computational techniques, such as MD simulations and pharmacophore modeling, can speed up drug discovery, especially in the fight against antibiotic resistance

Diesel biodegradation capacities of indigenous bacterial species isolated from diesel contaminated soil

Abstract Petroleum based products are the major source of energy for industries and daily life. Leaks and accidental spills occur regularly during the exploration, production, refining, transport, and storage of petroleum and petroleum products. In the present study we isolated the bacteria from diesel contaminated soil and screened them for diesel biodegradation capacity. One monoculture isolate identified by 16S rRNA gene sequence analysis to be Acinetobacter baumannii was further studied for diesel oil biodegradation. The effects of various culture parameters (pH, temperature, NaCl concentrations, initial hydrocarbon concentration, initial inoculum size, role of chemical surfactant, and role of carbon and nitrogen sources) on biodegradation of diesel oil were evaluated. Optimal diesel oil biodegradation by A. baumanii occurred at initial pH 7, 35°C and initial hydrocarbon concentration at 4%. The biodegradation products under optimal cultural conditions were analyzed by GC-MS. The present study suggests that A. baumannii can be used for effective degradation of diesel oil from industrial effluents contaminated with diesel oil.</jats:p

Comparisons of multiple-impact laws for multibody systems: Moreau's law, binary impacts, and the LZB approach

International audienceThis chapter is dedicated to the comparison of three well-known models that apply to multiple (that is, simultaneous) collisions: Moreau's law, the binary collision law, and the LZB model. First a brief recall of these three models and of their numerical implementation is done. Then an analysis based on numerical simulations, where the LZB outcome is considered as the reference outcome, is made. It is shown that Moreau's law and the binary collision model possess good prediction capabilities in some few "extreme" cases. The comparisons are made for free chains of aligned grains, and for chains impacting a wall. The elasticity coefficient, restitution coefficients, mass ratios and contact equivalent stiffnesses are used as varying parameters