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Computational aided virtual screening of natural epiesteriol | 57176

Zeitschrift für Mikrobiologie und Immunologie

Abstrakt

Computational aided virtual screening of natural epiesteriol as probable lead molecules towards prospective targets of multidrug resistant Acinetobacter baumannii

Sinosh Skariyachan

Multidrug resistant Acinetobacter baumannii (MDRAb) declared as priority-I pathogen by WHO (2017) and screening of potential therapeutic agents has profound application. This study aimed to identify putative drug targets of MDRAb and validate the therapeutic potential of natural molecules by structure based drug screening and in vitro studies. Ten clinical isolates of Ab were subjected to antibiotic susceptibility testing against five carbapenems and two colistins. Based on the metabolic pathway and functional role analysis, Omp38, RecA, PyrE and PyrF were identified as potential drug targets by KEGG database search. The three dimensional structure of Omp38 was retrieved from PDB and others were computationally predicted and validated. 236 natural molecules were screened from various databases and subjected to virtual screening, molecular docking and molecular dynamic simulation. The therapeutic potential of computationally predicted molecules was validated by in vitro studies. The clinical isolates (n=10) showed extreme dug resistance to carbapenems and colistins (p<0.05). Computational screening suggested that 06 leads were qualified for drug likeliness, pharmacokinetic features and one molecule-natural epiesteriol (16b-Hydroxy-17a-estradiol) exhibited significant binding towards four drug targets in comparison with the binding of faropenem and polymyxin E towards their usual targets. MD simulations suggested that epiesteriol- receptor complexes demonstrated stability throughout the simulation. The growth curve and time kill assays revealed that MDRAb showed resistance to Faropenem and Polymyxin- E and the purified epiesteriol showed significant inhibitory properties (100 μg/mL) towards four drug targets in comparison with the controls (p≤0.5).