Pharmacoinformatics-based prediction of Checkpoint kinase-1 inhibitors from Momordica charantia Linn. for cancer

Abstract

Checkpoint kinase 1 (Chk-1), a serine/threonine kinase family protein, is an emerging target in cancer research owing to its crucial role in cell cycle arrest. Therefore, we aimed to predict potential Chk-1 inhibitors from Momordica charantia Linn., using high-throughput molecular docking. We used a graph theoretical network approach to determine the target protein, Chk-1. Among 86 compounds identified from M. charantia L., five molecules such as α-spinasterol (-9.7 kcal × mol⁻¹), stigmasterol (-9.6 kcal × mol⁻¹), stigmasta-7,22,25-trienol (-9.5 kcal × mol⁻¹), campesterol (-9.5 kcal × mol⁻¹), and stigmasta-7,25-dien-3beta-ol (-9.5 kcal × mol⁻¹) and standard drug CCT245737 (-8.3 kcal × mol^-1) displayed highest binding affinity with Chk-1. Besides, pharmacokinetic studies have demonstrated the non-toxic and drug-like properties of these compounds. Furthermore, molecular dynamics (MD) simulation studies confirmed the strong intermolecular interactions and stability of the compounds with Chk-1. The estimation of binding free-energy derived from molecular docking was fully recognized by the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) produced from the MD simulation paths. Altogether, these five compounds may serve as effective inhibitors of Chk-1, thereby could be used to develop new medications for cancer treatment.