Background: This study investigates the role of peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) in tumorigenesis and evaluates the potential of novel PIN1 inhibitors for cancer therapeutics. The design integrates computational approaches, including three-dimensional quantitative structure–activity relationship modeling, molecular docking, and molecular dynamics simulations, to develop and assess new inhibitors targeting PIN1. A dataset of 26 derivatives was utilized to construct predictive models and design potent inhibitors. Methods: First, a Comparative Molecular Similarity Indices Analysis model was constructed, incorporating steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields to develop a predictive model for PIN1 inhibitors. Molecular docking was then performed to predict binding affinity between the inhibitors and PIN1, followed by molecular dynamics simulations to assess the stability of the inhibitors. Energy decomposition analysis identified key residues involved in binding, providing insights for molecular optimization. Results: The Comparative Molecular Similarity Indices Analysis model showed good predictive ability, with a cross-validated q 2 of 0.630 and a non-cross validated r 2 of 0.925. The top optimized compound showed a predicted pIC50 of 9.962, indicating strong inhibitory activity. Molecular docking confirmed strong binding affinity between the inhibitor and PIN1. Molecular dynamics simulations demonstrated the compound’s stability at the binding site, and energy decomposition analysis revealed key residues contributing to binding. Conclusion: The integration of computational techniques highlights a rational approach to the design of potent PIN1 inhibitors, offering a promising foundation for further development in cancer therapeutics.
Xu et al. (Fri,) studied this question.