Identification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products

3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has emerged as a promising anticancer target. In this study, a library of 3,744 natural products was virtually screened against human 3-MST using machine learning-based screening and molecular docking. Top-ranking candidates were further analyzed via molecular dynamics (MD) simulations and MM-PBSA binding free energy calculations. Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. In vitro cytotoxicity assays showed that, Methylophiopogonanone A and L-asarinin reduced HCT116 cell lines viability by 40.3% and 26.3% at 25 µM, which is consist with their inhibitory to 3-MST with IC ₅₀  = 5.83 ± 0.69 µM and IC ₅₀  = 19.11 ± 3.37 µM, respectively. These results suggested that the natural products identified in this study should be promising start point for the development of novel anticancer agents targeting 3-MST.