Fragment-Based Drug Discovery of KIF11 Inhibitors for Glioblastoma Treatment: Molecular Insights and Therapeutic Potential

Graphical Abstract

Fragment based novel drug identification and its validation through use of molecular dynamics and simulations.

Abstract

Comparing primary microcephaly genes with glioblastoma expression profiles reveals potential oncogenes, with proteins that support growth and survival in neural stem/progenitor cells likely retaining critical roles in glioblastoma. Identifying such proteins in familial and congenital microcephalic disorders offers promising targets for brain tumor therapy. Among these, KIF11, a kinesin motor protein (KSP), stands out as a significant oncogene. Expression analyses across various cancer types, including glioblastoma, demonstrate its overexpression in brain tumor patients. Using a targeted fragment-based drug discovery approach, we explored alternative small molecule inhibitors for KIF11. Existing drugs, such as ispinesib, are limited by side effects and multidrug resistance. Through molecular docking and simulations, we identified three candidate drug fragments. Further analysis confirmed that Mol-121026 exhibits a more stable interaction with KIF11 compared to ispinesib. Detailed analyses indicate that Mol-121026 binds to the same active site as the reference drug, effectively inhibiting KIF11ʼs mechano-chemical activity. Importantly, Mol-121026, a derivative of 3-phenyl-1H-pyrazol-5-carboxylic acid, offers a promising alternative due to its lower molecular complexity, ability to target allosteric sites, and potential for optimization into a potent and effective drug candidate. Our findings identified Mol-121026 as a top candidate with a docking score of −10.2 kcal/mol and MM/GBSA binding energy of −19.10 kcal/mol. Molecular dynamics simulations revealed stable interactions with key residues GLU116 and GLU118, supporting its potential as a promising KIF11 inhibitor.

Keywords

drug research - drug regulation - anticancer drugs - cancer - central nervous system disorders

^# QAN and CX should be considered joint first authors: Qais Ahmad Naseer, Cao Xuexian

Publikationsverlauf

Eingereicht: 08. November 2024

Angenommen: 05. Januar 2025

Artikel online veröffentlicht:
22. Januar 2025

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