Structure-Based Virtual Screening, Insilico Docking, ADME Prediction, and Molecular Dynamics Studies for Identification of Potential NMT Inhibitors Against Malaria

Abstract

One of the major public health problems globally, malaria, is mainly caused protozoan parasites from the genus Plasmodium, and commonly spreads to people through the bites of infected female mosquitoes of the genus Anopheles. Strategies for treatment, prevention, and control are available for malaria but the eradication of malaria still poses great challenge due to plasmodium’s drug resistance over the past decades. Development of novel antimalarial drugs remains a significant task to protect people from malaria. N-Myristoyl transferase is responsible for the N-Myristoylation catalysis process and the survival of Plasmodium species. Thus, it is considered a therapeutic drug target in protozoans and was recently validated as a significant target for Plasmodium vivax. In this present scenario, we endeavour to identify effective NMT inhibitors to prevent the onset of malaria in the human species. Initially, the structure-based virtual screening was executed against ZINC database and four potential candidates for NMT were identified. Furthermore, the four identified compounds were subjected to ADME prediction and all the four compounds found within adequate range with predicted ADME properties. Eventually, we conducted the molecular dynamics simulation to investigate the binding stability of top three protein-ligand complexes at different time scale by employing the tool Desmond. The molecular dynamics simulation studies revealed the protein-ligand complexes were stable throughout the entire simulation. Besides, we noticed that the residues ASN 365, PHE 103 and HIS 213 of NMT were crucially involved in the formation of various intermolecular interactions, significantly contributing to the stability of protein-ligand complexes. From this computational investigation, we suggest that the three identified potential compounds are extremely useful for further lead optimization and drug development.

Computational Exploration of gp63: Unlocking Potential Inhibitors for Leishmaniasis

“…Additionally, a grid that depicts the active site's size and location enables more precise grading of the ligand poses [25].…”