A Computational Biophysical Approach to Inhibit the Oncogenic Activity of CRIPTO Protein in Human Cancers

ABSTRACT Here, we present a comprehensive computational evaluation of three FDA (Food and Drug Administration) approved drug leads, ZINC150338755, ZINC6716957, and ZINC203686879, as potential inhibitors targeting the Cripto/FRL‐1/Cryptic (CFC) domain of the CRIPTO (Teratocarcinoma‐derived growth factor1) protein. CRIPTO is a key oncogenic protein implicated in tumor progression, metastasis, and therapy resistance across multiple cancer types. Its overexpression promotes cancer stemness and survival in various tumors, such as liver cancer and glioblastoma, while its restricted expression in normal tissues makes it an attractive therapeutic target. Through an integrated approach merging molecular docking (with results −8.6 to −9.1 kcal/mol affinities), molecular dynamics (MD) simulations, molecular mechanics–generalized Born surface area (MM‐GBSA) binding free energy calculations, and free energy landscape (FEL) analysis, we delineate distinct binding modes and thermodynamic fingerprints of the inhibitor complexes. Virtual screening determined the lead compounds, which were subjected to 250 ns MD simulations to check the stability and dynamics of interactions. Binding free energy calculation revealed striking disparities in binding energies (ΔG bind from −26.83 to −57.37 kcal/mol), where Complex1 exhibited increased stability through hydrophobic superiority, Complex2 showed similar polar/nonpolar interactions, and Complex3 exhibited unique electrostatic‐driven recognition. These findings shed atomic‐level insight into CRIPTO‐inhibitor interactions and offer a solid foundation for structure‐based optimization of CRIPTO inhibitors.