Discovery and Characterization of Two Selective Inhibitors for a Mu-Class Glutathione S-Transferase of 25 KDa from Using Computational and Bioinformatics Tools

Glutathione S-transferases (GSTs) are promising pharmacological targets for developing antiparasitic agents against helminths, as they play a key role in detoxifying cytotoxic xenobiotics and managing oxidative stress. Inhibiting GST activity can compromise parasite viability. This study reports the successful identification of two selective inhibitors for the mu-class glutathione S-transferase of 25 kDa (Ts25GST) from , named and , using a computationally guided approach. The workflow involved modeling and refining the 3D structure from the sequence using the AlphaFold algorithm and all-atom molecular dynamics simulations with an explicit solvent. Representative structures from these simulations and a putative binding site with low conservation relative to human GSTs, identified via the SILCS methodology, were employed for virtual screening through ensemble docking against a commercial compound library. The two compounds were found to reduce the enzyme's activity by 50-70% under assay conditions, while showing a reduction of only 30-35% for human mu-class GSTM1, demonstrating selectivity for Ts25GST. Notable, displayed competitive inhibition with CDNB, while exhibited a non-competitive inhibition type.