In-silico natural product database mining for novel neuropilin-1 inhibitors: molecular docking, molecular dynamics and binding energy computations

Abstract

In the search for new metabolite inhibitors, a natural product activity and species source (NPASS) database was virtually screened using AutoDock software to identify potential NRP1 inhibitors. NPASS compounds complexed with NRP1 were subjected to molecular dynamics (MD) simulations. Furthermore, NPASS-NRP1 binding affinities were calculated using the MM-GBSA approach. Based on calculated binding energies, kamolonol (NPC146388) demonstrated lower NRP1 binding affinity than the co-crystallized HRG/Arg-1 ligand with binding energy (ΔG binding) values of –34.5 and –32.0 kcal/mol, respectively. Structural and energetic analysis showed high stability for kamolonol and HRG/Arg-1 with NRP1 over the 200 ns MD simulations. The studied physicochemical properties of kamolonol and HRG/Arg-1 revealed that these compounds obey Lipinski's rule of five. ADMET characteristics of kamolonol and HRG/Arg-1 were predicted, and kamolonol showed better ADMET properties compared to HRG/Arg-1. Based on these results, kamolonol is a promising NRP1 inhibitor worthy of further experimental assays as anti-carcinoma remediation.