Targeting ME49 TgAPN2: A Bioinformatics Approach for Antiparasitic Drug Discovery.

As fewer therapeutic options are available for treating toxoplasmosis, newer antiparasitic drugs that can block TgAPN2 M1 aminopeptidase are of significant value. Herein, we employed several computer-aided drug-design approaches with the objective of identifying drug molecules from the Asinex library with stable conformation and binding energy scores. By a structure-based virtual screening process, three molecules-LAS_52160953, LAS_51177972, and LAS_52506311-were identified as promising candidates, with binding affinity scores of -8.6 kcal/mol, -8.5 kcal/mol, and -8.3 kcal/mol, respectively. The compounds produced balanced interacting networks of hydrophilic and hydrophobic interactions, vital for holding the compounds at the docked cavity and stable binding conformation. The docked compound complexes with TgAPN2 were further subjected to molecular dynamic simulations that revealed mean RMSD for the LAS_52160953 complex of 1.45 Å), LAS_51177972 complex 1.02 Å, and LAS_52506311 complex 1.087 Å. Another round of binding free energy validation by MM-GBSA/MM-PBSA was done to confirm docking and simulation findings. The analysis predicted average MM-GBSA value of <-36 kcal/mol and <-35 kcal/mol by MM-PBSA. The compounds were further classified as appropriate candidates to be used as drug-like molecules and showed favorable pharmacokinetics. The shortlisted compounds showed promising biological potency against the TgAPN2 enzyme and may be used in experimental validation. They may also serve as parent structures to design novel derivatives with enhanced biological potency.