Phytocompounds from Himalayan Medicinal Plants as Potential Drugs to Treat Multidrug-Resistant Salmonella typhimurium: An In Silico Approach.
Jyoti MehtaRajan RoltaDeeksha SalariaOladoja AwofisayoOlatomide A FadarePrem Prakash SharmaBrijesh RathiAdity ChopraNeha KaushikEun Ha ChoiNagendra Kumar KaushikPublished in: Biomedicines (2021)
Medicinal plants can be used as natural therapeutics to treat diseases in humans. Enteric bacteria possess efflux pumps to remove bile salts from cells to avoid potential membrane damage. Resistance to bile and antibiotics is associated with the survival of Salmonella enterica subspecies enterica serovar Typhimurium (S. typhimurium) within a host. The present study aimed to investigate the binding affinity of major phytocompounds derived from 35 medicinal plants of the North Western Himalayas with the RamR protein (PDB ID 6IE9) of S. typhimurium. Proteins and ligands were prepared using AutoDock software 1.5.6. Molecular docking was performed using AutoDock Vina and MD simulation was performed at 100 ns. Drug likeness and toxicity predictions of hit phytocompounds were evaluated using molinspiration and ProTox II online servers. Moreover, docking, drug likeness, and toxicity results revealed that among all the selected phytocompounds, beta-sitosterol exhibited the most efficacious binding affinity with RamR protein (PDB ID 6IE9) and was nontoxic in nature. MD simulation data revealed that beta-sitosterol in complex with 6IE9 can be used as an antimicrobial. Furthermore, beta-sitosterol is stable in the binding pocket of the target protein; hence, it can be further explored as a drug to inhibit resistance-nodulation-division efflux pumps.
Keyphrases
- listeria monocytogenes
- molecular docking
- protein protein
- binding protein
- multidrug resistant
- molecular dynamics simulations
- molecular dynamics
- oxidative stress
- small molecule
- induced apoptosis
- amino acid
- zika virus
- staphylococcus aureus
- emergency department
- healthcare
- electronic health record
- signaling pathway
- adverse drug
- drug resistant
- cell cycle arrest
- cell death
- cell proliferation
- big data
- health information
- deep learning
- pseudomonas aeruginosa
- artificial intelligence
- cystic fibrosis