Justice Josiah Mallen, Shilpa Sharma, Md Nazmul Hasan, Arjun Saha
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引用次数: 0
Abstract
Kinases are pivotal in regulating signaling pathways, and their dysregulation is associated with various diseases, including cancers, making them prime therapeutic targets. Bruton's Tyrosine Kinase (BTK) is crucial for B-cell development, and BTK inhibitors have proven effective in treating B-cell malignancies like Chronic Lymphocytic Leukemia (CLL). Non-covalent inhibitors offer a promising therapeutic approach by avoiding covalent bond formation with the protein. However, therapeutic resistance due to BTK mutations in the catalytic domain has led to relapses and refractory cases in CLL, highlighting the need for a deeper understanding of these mutations' impact on treatment outcomes. This study investigates the effects of four prevalent single-point mutations-A428D, T474I, C481S, and L528W-within the catalytic domain of BTK. Using 12.5 microseconds of molecular dynamics simulations and computational drug discovery methods, we examine how these mutations influence the binding affinities and interactions of non-covalent BTK inhibitors. Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analysis showed that mutant forms of BTK significantly decreased ligand binding free energies compared to the wild types, with a few exceptions. With pocket volume and solvent-accessible surface area analysis, we also show that mutations reduce the binding pocket volume, forcing the inhibitors to move out of the pocket, disrupting the critical non-covalent interactions of the inhibitors with mutant BTK. This confirms the experimental and clinical observations of why these BTK mutations impair inhibitor efficacy fostering drug resistance. Our results offer vital insights for designing next-generation BTK inhibitors to overcome resistance and enhance therapeutic outcomes in B-cell malignancies.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.