Identification of Der p1 inhibitors: Combining AI-based QSAR, molecular docking, molecular dynamics simulations and quantum physics.
TL;DR
The integrated computational workflow identified Betrixaban as the most promising FDA-approved candidate for Der p1 inhibition based on its favorable overall binding affinity and stable protein complex, while Ceritinib demonstrated superior electronic interactions and hydrogen-bond stability that may support future lead optimization.
Abstract
Immunotoxins represent promising therapeutics; they can be used as allergen-specific therapies. Der p1 is a major allergen produced by the house dust mite Dermatophagoides pteronyssinus responsible for allergic asthma and other allergic diseases. In spite of clinical relatedness, no therapies have been approved at the molecular level. This work is established to identify potential inhibitors against Der p1 via a multi-tiered in silico approach. A library of FDA-approved drugs of 2855 drugs was evaluated using AI-driven QSAR models built from molecular descriptors to pIC50. Top-ranked (93 drugs) were docked into the Der p1's binding site to assort drugs based on binding affinity. Molecular docking results revealed that the top three docking scores were -25.5456, -20.6206 and -19.9821 kcal/mol for betrixaban, ceritinib and ivacaftor, respectively. Top-docking-scored drugs were subjected to 100-ns MD simulations to analyze dynamic stability and conformational behavior. Betrixaban exhibited the lowest average protein RMSD (0.136 nm), indicating the highest receptor stability, although it displayed greater ligand flexibility than the other compounds. MM-GBSA calculations identified betrixaban as the strongest binder, with the most favorable average binding free energy, primarily driven by van der Waals, electrostatic, and lipophilic interactions. Ceritinib showed the most favorable QM/MM energy (-2463.62), the highest hydrogen bonds with peaks of (6-7), and the lowest ligand RMSD, indicating stronger localized electronic interactions within the active site. In every computational analysis, ivacaftor exhibited intermediate binding behavior. The integrated computational workflow identified Betrixaban as the most promising FDA-approved candidate for Der p1 inhibition based on its favorable overall binding affinity and stable protein complex, while Ceritinib demonstrated superior electronic interactions and hydrogen-bond stability that may support future lead optimization. These findings provide a rational framework for drug repurposing against Der p1 and warrant further experimental validation through biochemical and cellular assays.