Zheng Shuang, Wang Yin, Tang Shuainan, Guo Yuntao, Ma Duan, Jiang Xin
School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Precision Genes Technology, INC., Nantong, China.
Front Pharmacol. 2025 Mar 13;16:1549953. doi: 10.3389/fphar.2025.1549953. eCollection 2025.
Nimodipine has shown neuroprotective effects in several studies; however, the specific targets and mechanisms remain unclear. This study aims to explore the potential targets and mechanisms of nimodipine in the treatment of neurodegenerative diseases (NDDs), providing a theoretical foundation for repurposing nimodipine for NDDs.
Drug-related targets were predicted using SwissTargetPrediction and integrated with results from CTD, GeneCards, and DrugBank. These targets were then cross-referenced with disease-related targets retrieved from CTD to identify overlapping targets. The intersecting targets were imported into STRING to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the R package ClusterProfiler. Molecular docking was carried out using AutoDock Vina, and the ligand-receptor complexes with the highest binding affinities were further simulated using GROMACS to assess the dynamic structural stability and interactions between the ligand and receptor in the dynamic system.
A total of 33 intersecting drug-disease targets were identified. After constructing the PPI network and removing isolated targets, the network contained 28 nodes and 69 edges. Network degree analysis combined with enrichment analysis highlighted 12 key targets: CASP3, TNF, BAX, BCL2, IL1B, GSK3B, IL1A, MAOB, MAOA, BDNF, APP, and GFAP. Molecular docking analysis revealed binding energies greater than -6 kcal/mol for MAOA, GSK3B, MAOB, CASP3, BCL2, IL1B and APP. MAOA, with the highest binding energy of -7.343 kcal/mol, demonstrated a stable structure in a 100ns dynamic simulation with nimodipine, exhibiting an average dynamic binding energy of -52.39 ± 3.05 kcal/mol. The dynamic cross-correlation matrix (DCCM) of nimodipine resembled that of harmine, reducing the interactions between protein residues compared to the apo state (regardless of positive or negative correlations). Furthermore, nimodipine induced new negative correlations in residues 100-200 and 300-400.
Nimodipine binds to the internal pocket of MAOA and shows potential inhibitory effects. Given its brain-enrichment characteristics and proven neuroprotective effects, it is hypothesized that nimodipine may exert therapeutic effects on NDDs by inhibiting MAOA activity and modulating cerebral oxidative stress. Thus, MAOA emerges as a promising new target for nimodipine in the treatment of NDDs.
尼莫地平在多项研究中已显示出神经保护作用；然而，具体靶点和机制仍不清楚。本研究旨在探索尼莫地平治疗神经退行性疾病（NDDs）的潜在靶点和机制，为将尼莫地平重新用于治疗NDDs提供理论基础。
使用SwissTargetPrediction预测与药物相关的靶点，并与CTD、GeneCards和DrugBank的结果进行整合。然后将这些靶点与从CTD检索到的疾病相关靶点进行交叉参考，以识别重叠靶点。将交叉靶点导入STRING以构建蛋白质-蛋白质相互作用（PPI）网络。使用R包ClusterProfiler进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。使用AutoDock Vina进行分子对接，并使用GROMACS进一步模拟具有最高结合亲和力的配体-受体复合物，以评估动态系统中配体与受体之间的动态结构稳定性和相互作用。
共鉴定出33个交叉的药物-疾病靶点。构建PPI网络并去除孤立靶点后，该网络包含28个节点和69条边。网络度分析与富集分析突出了12个关键靶点：CASP3、TNF、BAX、BCL2、IL1B、GSK3B、IL1A、MAOB、MAOA、BDNF、APP和GFAP。分子对接分析显示，MAOA、GSK3B、MAOB、CASP3、BCL2、IL1B和APP的结合能大于-6 kcal/mol。MAOA的结合能最高，为-7.343 kcal/mol，在与尼莫地平的100ns动态模拟中显示出稳定的结构，平均动态结合能为-52.39±3.05 kcal/mol。尼莫地平的动态交叉相关矩阵（DCCM）与 harmine的相似，与无配体状态相比，减少了蛋白质残基之间的相互作用（无论正相关还是负相关）。此外，尼莫地平在残基100-200和300-400中诱导了新的负相关。
尼莫地平与MAOA的内部口袋结合并显示出潜在的抑制作用。鉴于其脑富集特性和已证实的神经保护作用，推测尼莫地平可能通过抑制MAOA活性和调节脑氧化应激对NDDs发挥治疗作用。因此，MAOA成为尼莫地平治疗NDDs的一个有前景的新靶点。
