Computational phytochemical screening for Parkinson's disease therapeutics: c-Abl and beyond

IF 2.6 4区生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2025-02-07 DOI:10.1016/j.compbiolchem.2025.108370

Jesmina Yasmine , Piyong Sola , Emdormi Rymbai , Bhaskar Jyoti Dutta , Sankarkishor Buragohain

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Abstract

Parkinson's disease (PD), a rapidly growing neurodegenerative disorder, is characterized by intracellular α-synuclein aggregates. The tyrosine kinase c-Abl plays a critical role in PD pathogenesis. This study aimed to identify novel c-Abl inhibitors from natural products using molecular docking and dynamics simulations. We explored phytochemicals from Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database and employed molecular docking and molecular dynamics to discover c-Abl inhibitors. Three potential hits: IMPHY008934, IMPHY009589, and IMPHY006310 were identified. These compounds demonstrated comparable binding affinity to Nilotinib, a comparison drug. Toxicity predictions revealed IMPHY008934 and IMPHY009589 exhibited lower toxicity than Nilotinib. Molecular dynamics simulations confirmed the stability of IMPHY009589 and IMPHY008934 with c-Abl. Density functional theory (DFT) analysis showed that IMPHY006310 and IMPHY008934 displayed enhanced reactivity and polarizability. Our findings suggest these natural compounds may target c-Abl in PD pathogenesis and possibly downregulate the overexpressed α-synuclein and may serve as promising leads for PD therapy.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.