Molecular docking and network pharmacology analyses of Shalin Decoction in regulating crystal homeostasis: active components and multi-target mechanisms

doi:10.12307/2026.906

Abstract

Abstract: BACKGROUND: Shalin Decoction, a traditional Chinese medicine formula, has been shown to significantly inhibit crystal formation. Although preliminary studies have revealed the multi-target regulatory potential of its compositions, the systemic regulatory network underlying its therapeutic effects remains to be fully elucidated.
OBJECTIVE: To identify the potential active components, target proteins, and their interaction networks in Shalin decoction using network pharmacology and molecular docking approaches, and to explore its potential molecular mechanisms in treating kidney stones.
METHODS: The active compounds of Shalin Decoction were retrieved from the TCMSP database (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform), and potential drug-like molecules were screened. Candidate targets of Shalin Decoction were identified by intersecting with kidney stone-related genes from the GeneCard database. A protein-protein interaction network was then constructed using the STRING database, and core targets were extracted for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Tissue-specific expression of core genes was analyzed using the BioGPS database. Molecular docking was further employed to visualize the binding affinities between potential targets and their corresponding compounds.
RESULTS AND CONCLUSION: A total of 15 potential active ingredients and 74 corresponding targets of Shalin Decoction were screened from the TCMSP database. Protein-protein interaction network analysis identified interleukin-6, caspase-3, tumor necrosis factor, proto-oncogene protein JUN, B-cell lymphoma 2, peroxisome proliferator-activated receptor gamma, transforming growth factor beta 1, and proto-oncogene protein FOS as the core targets with the highest degree centrality. Enrichment analysis indicated that these targets were significantly associated with kidney stone-related biological processes and Kyoto Encyclopedia of Genes and Genomes pathways. Molecular docking further confirmed that hydrogen bonding and hydrophobic interactions were key forces mediating the stable binding between active compounds and their target proteins.

Key words: Shalin Decoction, kidney stones, multi-target mechanism, molecular docking, target protein

CLC Number:

Su Gangling, Liu Chunying, Cui Yangqing, Zhou Shaohu, Liu Shiyong, Wu Zhimin. Molecular docking and network pharmacology analyses of Shalin Decoction in regulating crystal homeostasis: active components and multi-target mechanisms[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(36): 9613-9620.

Figures/Tables 7

素6(IL6)、肿瘤坏死因子(TNF)、半胱天冬酶3(CASP3)和原癌基因蛋白JUN。选取度值最高的节点构建核心蛋白质-蛋白质相互作用网络(图2B)，各核心节点的具体度值如图2C所示。采用MCODE插件对蛋白质-蛋白质相互作用网络进行聚类分析，共得到3个主要聚类模块：CYPs-ARs聚类、AR-CYPs聚类及VCAM1-MMP1聚类(图2D-F)。上述结果表明，砂淋汤的靶基因可能通过调控肾结石形成过程中的关键通路发挥作用。 2.3 基因本体GO功能富集分析为明确砂淋汤在肾结石预防与治疗中的作用，选取蛋白质-蛋白质相互作用网络中度值排名前50的核心靶基因进行GO功能富集分析，将富集结果分为生物学过程(BP)、分子功能(MF)和细胞组分(CC)三个维度，通过气泡图可视化(图3A-C)。生物学过程：最显著富集的条目为“外源性物质刺激反应(response to xenobiotic stimulus)”和“营养水平反应(response to nutrient levels)”；分子功能：富集最显著的条目集中在转录调控相关功能，包括“DNA结合转录因子结合(DNA-binding transcription factor binding)”和“RNA聚合酶特异性DNA结合转录(RNA polymerase-specific DNA binding transcription)”。根据基因计数(count)和P值对富集条目进行排序，结果如图3D，E所示。上述结果进一步证实，砂淋汤的核心靶基因主要富集于与肾结石发生发展相关的功能通路。 2.4 KEGG 富集分析对蛋白质-蛋白质相互作用网络中度值前50的核心靶基因进行KEGG通路富集分析，共涉及359条经典通路，其中109条通路达到显著富集水平(假发现率FDR < 0.01)。选取富集最显著的15条通路，通过气泡图展示其校正后P值(adjusted P-value)与基因比例(图4A)，这些通路包括“脂质与动脉粥样硬化通路(lipid and atherosclerosis)”“糖尿病并发症中的AGE-RAGE信号通路(AGE?RAGE signaling pathway in diabetic complications)”及多条免疫相关通路(图4D)。此外，构建“砂淋汤活性成分-靶基因-富集通路”关联网络(图4C)，该网络包含414条边和119个节点，其中山柰酚(MOL000422)和蔗糖(sucrose，编号 MOL000843)是连接靶基因数量最多的活性成分，蛋白激酶Cα(PRKCA)和丝裂原活化蛋白激酶8(MAPK8)为核心靶基因。采用桑基图可视化富集通路与靶基因的关联关系(图4B)。以上结果表明，砂淋汤可能通过调控多条经典通路发挥抗肾结石作用。 2.5 器官/组织-靶基因网络分析检测74个交集靶基因在84种组织/器官中的表达水平，取重复样本的平均表达值进行z值转换，将z值> 3的基因定义为在该组织/器官中“特异性表达”。基于此构建并可视化器官/组织-靶基因网络(图5)，该网络包含112个节点(74个靶基因+38种组织/器官)和123条边，其中度值最高的组织/器官依次为：肝脏(Liver，度值10)、CD105阳性内皮细胞(CD105._Endothelial，度值8)、B淋巴细胞(B_lymphoblasts，度值8)、平滑肌(SmoothMuscle，度值7)、CD33阳性髓系细胞(CD33._Myeloid，度值7)、CD14阳性单核细胞(CD14._Monocytes，度值6)、前额叶皮质(PrefrontalCortex，度值4)、视网膜(retina，度值4)、CD71阳性早幼红细胞(CD71._EarlyErythroid，度值4)、胎盘(Placenta，度值4)。结果显示，多种组织/器官可同时表达多个靶基因，且多数靶基因在两种及以上组织/器官中表达，提示肾结石的形成涉及多组织/器官协同作用；值得注意的是，肝脏等代谢相关组织及免疫细胞的度值最高，表明这些组织/器官在肾结石发生过程中可能发挥关键作用。 2.6 分子对接验证为验证网络药理学分析结果的可靠性，通过分子对接评估砂淋汤活性成分与核心靶蛋白的结合能力。基于蛋白质-蛋白质相互作用网络的节点度值，选取4个核心靶基因(JUN、IL6、CASP3、TNF)编码的蛋白进行对接实验。结果显示，与其他蛋白相比，JUN、IL-6、CASP3、TNF蛋白与对应活性成分的对接得分更低(图6A-D)，提示这些蛋白与活性成分的结合亲和力更强，可能是砂淋汤发挥抗肾结石作用的潜在关键靶标。"

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