Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents

• Published in: Journal of biomolecular structure & dynamics 2018-10, Vol.36 (14), p.3687-3704
• Main Authors: Balakumar, Chandrasekaran, Ramesh, Muthusamy, Tham, Chuin Lean, Khathi, Samukelisiwe Pretty, Kozielski, Frank, Srinivasulu, Cherukupalli, Hampannavar, Girish A., Sayyad, Nisar, Soliman, Mahmoud E., Karpoormath, Rajshekhar
• Format: Article
• Language: English
• Subjects: 3D, three-dimensional; ADP, adenosine diphosphate; AMBER, assisted model building with energy refinement; ATP, adenosine triphosphate; CB, chembridge; CDOCKER; CG, conjugate gradient; DS, discovery studio; GAFF, generalized amber force field; IBP, interaction-based pharmacophore model; KSP ATPase enzyme inhibition; KSP inhibitors; KSP, kinesin spindle protein; MB, maybridge; MD, molecular dynamics; MM/GBSA, molecular mechanics/generalized Born and surface area continuum solvation; molecular dynamics; MT, microtubule; PDB, protein data bank; pharmacophore modelling; RMSD, root mean square deviation; RMSF, root mean square fluctuations; RoG, radius of gyration; SASA, solvent accessible surface area; SD, steepest descent; vdW, van der Waals
• ISSN: 0739-1102; 1538-0254
• DOI: 10.1080/07391102.2017.1396255

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.