Fig. 8
Identification and validation of pyrithioxin as an ENO2-MIF interaction inhibitor and its anti-metastatic effect in vivo. a Workflow of the in silico screening strategy. A total of 6723 compounds were subjected to ADME/T evaluation, multilevel docking, MM-GBSA rescoring, and binding assays to identify candidate compounds with high affinity. b MM-GBSA binding free energy (ΔG_bind) values of the top-ranked compounds. Pyrithioxin dihydrochloride showed one of the most favorable binding energies. c Co-IP assay showing that compounds (#1, #2, and #3) disrupt the interaction between ENO2 and MIF. Flag-ENO2 was immunoprecipitated, and ubiquitination of MIF was detected in the presence of MG132 in DLD-1 cells. d Molecular docking model illustrating the binding mode of pyrithioxin within the ENO2–MIF interface, highlighting key interacting residues. e Molecular dynamics simulations of the ENO2–pyrithioxin complex showing RMSD changes over time and residuewise RMSF analysis, indicating stable binding. f Schematic of the in vivo experimental design. MC38 cells were injected on day 0, followed by oral gavage of PBS or pyrithioxin three times per week until sacrifice on day 21. g Representative images of liver metastases, H&E staining (2× and 10×), in vivo bioluminescence imaging, and quantitative analysis of total flux and number of liver metastatic nodules in mice treated with 0, 10, or 20 mg/kg pyrithioxin. h Immunoblot analysis of MIF, p-STAT3/STAT3, and p-P65/P65 levels in tumor tissues from DMSO- or pyrithioxin-treated groups, with β-actin as a loading control
