Using computational biology techniques to explore hTERT inhibitors as a therapeutic approach against cancer

Human telomerase is reactivated in approximately 90% of tumors through hTERT gene overexpression, frequently associated with promoter alterations, thereby conferring replicative immortality to neoplastic cells and making this enzyme a promising target for anticancer therapy development. In this context, an integrated computational workflow was developed to identify potential hTERT inhibitors, encompassing successive stages from structural characterization of the enzyme to the final prioritization of candidate compounds. Initially, two human telomerase systems, in the absence and presence of telomeric DNA, were prepared and subjected to molecular dynamics simulations. Structural stability and conformational dynamics analyses (RMSD, RMSF, and radius of gyration), complemented by principal component analysis (PCA) and clustering, enabled the characterization of the enzyme's conformational behavior and the identification of dominant conformational states. Although both systems exhibited structural stability, the representative conformation obtained from the DNA-free system was selected for virtual screening because it maintained the catalytic site accessible for ligand binding. Using this structure, a library of 50,238 compounds was screened by molecular docking with AutoDock Vina, and the top-ranked pose for each ligand was re-evaluated using the RF-Score v3 and PLEClinear rescoring methods. Integration of the three scoring functions through consensus ranking reduced the dependence on a single scoring function for candidate prioritization. The top 1% of ranked compounds (502 molecules) were first evaluated by chemical similarity analysis, demonstrating the preservation of structural diversity across chemical families, and subsequently subjected to protein–ligand interaction analysis using PLIP. Final candidate selection combined two complementary strategies, in which ten compounds were selected directly from the consensus ranking, while the remaining ten were prioritized using a weighted scoring scheme based on the interactions identified by PLIP, with all 20 selected molecules additionally undergoing visual inspection of binding modes to support interaction plausibility. Overall, the results demonstrate that integrating molecular dynamics, conformational selection, virtual screening, consensus scoring, and structural interaction analysis provides a robust strategy for ligand prioritization in biologically complex systems, yielding promising candidates for experimental validation and contributing to the rational development of novel telomerase inhibitors.