Identification of Natural Inhibitors Targeting the NS3-Like Helicase Enzyme of Emerging Tick Alongshan Virus

Abstract

In this study, considering the vital biological importance of the Alongshan virus NS3-like helicase enzyme, several machine learning and artificial intelligence-based software and servers were used to identify compounds that exhibited the best binding affinity for the helicase enzyme. The predicted compounds were MSID000152, MSID000165, MSID000200, AfroDb.28, and AfroDb.207 with binding energy scores of -9.7, -9.5, -9.4, -8.65, and -8.01 kcal/mol, respectively. Because static intermolecular confirmation is not highly valued in terms of docked stability, the results were validated through molecular dynamics simulation analysis within 100 ns. The MSID000152, MSID000165, and MSID000200 showed significant uniform dynamics with root mean square deviation (RMSD) values of <3 Å. The intermolecular interaction energies were estimated using two well-known methods: MMPBSA and WaterSwap. Both methods agreed regarding the appreciated intermolecular strength of the leads with the helicase enzyme. Van der Waals interactions were identified to be the dominant force in stabilizing the ligands with the helicase enzyme in all complexes. Similarly, the electrostatic energy supported the stable intermolecular conformation of the docked complexes. The selected compounds were drug-like and exhibited good pharmacokinetic properties.

Keywords

• alongshan virus
• ns3-like helicase enzyme
• structure based virtual screening
• ai
• molecular dynamics simulation