Identification and validation of key covalent inhibitors targeting lung cancer proteins through integrated In Silico...

Lung cancer remains the leading cause of cancer-related mortality worldwide, underscoring the urgent need for new and more effective therapeutics. In this study, an integrated in silico workflow was employed to identify and evaluate covalent inhibitors targeting three proteins implicated in lung cancer progression: Transcription Factor Dp-1 (TFDP1), Lipocalin 2 (LCN2), and Poly(rC)-Binding Protein 1 (PCBP1). An initial virtual screening of 369 covalent inhibitors resulted in 366 compounds meeting Lipinski's Rule of Five, indicating excellent drug-like properties for the majority of the library. Structure-based molecular docking using CovDock was conducted for each target, revealing several inhibitors with high binding affinity (up to -10.5 kcal/mol for TFDP1) and low RMSD values, ensuring favorable and stable interactions within the active sites. Detailed interaction analysis highlighted persistent hydrogen bonding, π-stacking, and van der Waals contacts with key amino acid residues critical for target inhibition.The top five inhibitors for each protein were further subjected to comprehensive molecular dynamics (MD) simulations using GROMACS to evaluate the stability of the protein-ligand complexes under physiological conditions. The MD simulations demonstrated that the backbone RMSD values stabilized within the 2.0-3.0 Å range and secondary structure elements were preserved throughout the simulation, indicating robust conformational stability. Protein-ligand contact profiles and ligand torsion angle analyses confirmed the persistence of crucial interactions and minimal conformational drift over the course of 250 ns. Overall, the workflow identified several promising covalent inhibitors with strong binding affinities, favorable drug-likeness, and stable dynamic behavior, providing a valuable foundation for further experimental validation and the development of novel lung cancer therapeutics.