基于网络药理学-分子对接-分子动力学模拟探索赤芍治疗脓毒症相关性脑病的多靶点机制

Exploring the Multi-Target Mechanisms of Paeoniae Radix Rubra in Treating Sepsis-Associated Encephalopathy Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

摘要

目的：通过网络药理学、分子对接和分子动力学模拟，探究赤芍（paeoniae radix rubra，PRR）治疗脓毒症相关性脑病（sepsis-associated encephalopathy，SAE）的潜在作用机制。方法：使用TCMSP数据库和HERB数据库收集PRR活性成分，并利用PubChem和Swiss Target Prediction获取成分对应的靶点。使用OMIM、TTD、GeneCards和DrugBank数据库获取SAE相关靶点，通过交集运算，得到PRR成分和SAE共有的靶点。使用STRING数据库构建蛋白质相互作用网络，利用Cytoscape软件结合MCC、Degree、MNC、EPC四种算法识别核心靶点。以交集靶点为研究对象，利用DAVID在线数据库进行基因本体(gene ontology，GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes，KEGG)富集分析。选取Degree值排名前10位活性成分与筛选得到的核心靶点进行分子对接验证，并进一步纳入代表性单萜苷类成分芍药苷进行补充验证。进一步利用GROMACS v2020.6对结合能最低的复合物进行100 ns的分子动力学（molecular dynamics，MD）模拟，评估结合稳定性。结果：共筛选出PRR活性成分76个，对应靶点650个，SAE相关靶点752个，PRR-SAE共同靶点80个。网络拓扑分析提示Baicalein、Kaempferol、Tryptanthrin、n-(4-Chlorobenzoyl)-melatonin、Albiflorin等可能为PRR干预SAE的重要活性成分。核心靶点涉及ALB、TNF、STAT3、HIF1A、MMP9等。GO富集分析获得542个条目，KEGG分析显示110条信号通路显著富集。Degree值前6位活性成分与核心靶点的分子对接结合能均小于-5.0 kcal·mol^-1，表明具有良好的结合亲和力。此外，代表性单萜苷类成分芍药苷虽未进入Degree值前10，但仍被纳入分子对接验证，并与多个核心靶点表现出良好结合活性。分子动力学模拟进一步证实复合物结构稳定，结合亲和力强。结论：该研究初步揭示PRR“多成分-多靶点-多通路”干预SAE的潜在分子机制，为其药理作用的深入研究提供理论支撑。

Abstract

Objective: To explore the potential mechanism of paeoniae radix rubra (PRR) in the treatment of sepsis-associated encephalopathy (SAE) through network pharmacology, molecular docking and molecular dynamics simulation. Methods: The active components of PRR were collected from the TCMSP and HERB databases, and the corresponding targets were obtained from the PubChem and Swiss Target Prediction databases. SAE-related targets were collected from the GeneCards, OMIM, TTD and DrugBank databases, respectively. Common targets between PRR and SAE were identified by intersection analysis. A protein-protein interaction (PPI) network was constructed using the STRING database and core targets were identified using Cytoscape software with four algorithms: MCC, Degree, MNC and EPC. GO and KEGG enrichment analyses were performed using the DAVID database. The top 10 active components ranked by Degree value and the screened core targets were subjected to molecular docking with the core targets, and paeoniflorin, a representative monoterpene glycoside of PRR, was additionally included for supplementary validation. Further, the complex with the lowest binding energy was selected for a 100 ns molecular dynamics (MD) simulation using GROMACS v2020.6 to evaluate the binding stability. Results: A total of 76 active components and 650 corresponding targets of PRR were identified, along with 752 SAE-related targets. 80 overlapping targets between PRR and SAE were identified. Network topology analysis suggested that Baicalein, Kaempferol, Tryptanthrin, n-(4-Chlorobenzoyl)-melatonin, and Albiflorin may represent important active components involved in the therapeutic effects of PRR against SAE. Core targets included ALB, TNF, STAT3, HIF1A, MMP9, etc. GO enrichment analysis obtained 542 terms, and KEGG analysis revealed 110 significantly enriched signaling pathways. The molecular docking results showed that the binding energies between the top 10 active components and core targets were all lower than -5.0 kcal·mol^-1, indicating favorable binding affinity. In addition, paeoniflorin, a representative monoterpene glycoside of PRR, was further included in molecular docking validation and exhibited favorable binding activity with multiple core targets. Molecular dynamics simulation further confirmed the structural stability of the complex. Conclusion: This study preliminarily revealed the potential molecular mechanism underlying the therapeutic effects of PRR on SAE through a multi-component, multi-target and multi-pathway mode, providing a theoretical basis for further pharmacological research.

关键词

赤芍 / 脓毒症相关性脑病 / 网络药理学 / 分子对接 / 分子动力学模拟

Key words

paeoniae radix rubra / sepsis-associated encephalopathy / network pharmacology / molecular docking / molecular dynamics simulation

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基金资助

河北北方学院省属高校基本科研业务费自然科学项目(JYT2025008)