Abstract
The activation of telomerase represents an early step in carcinogenesis. Increased telomerase expression in malignant tumors suggests that telomerase inactivation may represent a potential chemotherapeutic target. In this work, existing anticancer drugs were docked against telomerase reverse transcriptase (TERT) using a Lamarckian genetic algorithm (LGA). Autodock's scoring function was applied to each of the molecules in order to identify the inhibitor with the strongest pharmacological action. The structural insights provided by this study regarding binding poses and possible interactions, free energies of binding, and drug scores aided in the identification of potential inhibitory compounds. The ranks of the various ligands investigated were based on the final docked energy values. Among nine selected compounds, vindesine, temsirolimus, and cyclosporine were found to be more potent TERT inhibitors than the standard inhibitor, curcumin.
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