Issue #116: Exploring the immunomodulatory mechanisms of Astragalus membranaceus in esophageal cancer treatment through network pharmacology, molecular docking, and molecular dynamics simulation.

Signal of the Day

Exploring the immunomodulatory mechanisms of Astragalus membranaceus in esophageal cancer treatment through network pharmacology, molecular docking, and molecular dynamics simulation.

Esophageal cancer (EC) is one of the most aggressive malignancies with limited therapeutic options and poor prognosis. Astragalus membranaceus (AM), a traditional Chinese herbal medicine with established immunomodulatory properties, has demonstrated anti-tumor potential, but its systematic immunomodulatory mechanisms in EC remain unclear. Here, we employed an integrative strategy combining network pharmacology, survival analysis, and molecular dynamics simulation to elucidate AM’s immunotherapeutic mechanisms in EC. We identified 17 active compounds from AM and 445 target genes associated with them, Among these, 113 were classified as immune target genes (ITGs) with potential relevance to EC. Functional enrichment analysis revealed significant involvement of ITGs in TNF signaling, PI3K-AKT signaling, and PD-L1/PD-1 checkpoint pathways.The prognostic model constructed by five ITGs (AHR, AKT1, GPER1, IL4, and MAPK1) can be used as a reliable prognostic feature of EC and was validated in an independent cohort. Protein-protein interaction analysis identified eight core ITGs (TNF, RELA, IL6, NFKB1, JUN, AKT1, TP53, and IL1β) significantly associated with immune cell infiltration, immune checkpoint expression, and immunotherapy response. Molecular docking and molecular dynamics simulation confirmed stable binding of isorhamnetin to AKT1, revealing key interaction residues. These findings suggested the multi-compound, multi-target, and multi-pathway immunomodulatory mechanism of AM in EC, providing a prognostic tool for patient stratification and identifying the isorhamnetin-AKT1 axis as a potential therapeutic target.

Why this matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

Also Worth Reading

Exploring the mechanism of saffron in treating viral myocarditis using network pharmacology and molecular docking.

Viral myocarditis (VM) is a cardiovascular disorder that can lead to heart failure and cardiogenic shock. Saffron, a traditional Chinese medicinal herb, has shown therapeutic potential against VM in numerous studies. However, the mechanisms through which saffron exerts its effects on VM remain poorly understood. Thus, this study aimed to elucidate the active compounds, molecular targets, and signaling pathways involved in saffron’s therapeutic action against VM by employing network pharmacology and molecular docking approaches. The active compounds and corresponding targets of saffron were retrieved from the Traditional Chinese Medicine Systems Pharmacology database. VM-associated targets were sourced from the GeneCards database. Overlapping targets between saffron and VM were then identified. Protein-protein interaction networks were established and analyzed utilizing the STRING platform and Cytoscape software to determine core targets. Furthermore, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were carried out utilizing Bioconductor in R to explore the potential biological activities and signaling pathways through which saffron may act against VM. Finally, molecular docking and model visualization were carried out using AutoDock Tools and PyMOL open-source software. From the database, we identified 4 active compounds in saffron with potential effects against VM: crocetin, isorhamnetin, kaempferol, and quercetin. A total of 60 corresponding targets were observed, with TNF, IL-6, IL-1β, CXCL8, and JUN emerging as core targets. Kyoto encyclopedia of genes and genomes enrichment analysis revealed 155 regulatory signaling pathways, among which the TNF, AGE-RAGE, and IL-17 signaling pathways, lipid metabolism, and atherosclerosis were the most prominent. Molecular docking results indicated that quercetin showed the strongest binding affinity toward IL-1β and CXCL8. The therapeutic effect of saffron against VM is not driven by a single factor, but rather involves multiple active compounds, targets, and signaling pathways.

Unraveling the anti-neuroinflammatory mechanisms of Cervus cucumis polypeptide injection in Alzheimer’s disease: insights from network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation.

Objective Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with increasing global prevalence, in which neuroinflammation serves as a critical pathological driver exacerbating cognitive decline. While current therapies offer limited symptomatic relief, multi-target strategies are urgently needed. Cervus cucumis polypeptide injection (CCPI), a traditional Chinese medicine (TCM) formulation, has demonstrated anti-inflammatory properties; however, its mechanisms of action against AD remain unclear. This study aimed to elucidate the anti-AD potential mechanisms of CCPI using an integrated approach combining network pharmacology, molecular docking, molecular dynamics (MD) simulation, and experimental validation. Methods Active components and corresponding targets of CCPI were retrieved from the TCMSP database, while AD-related targets were collected from Genecards, OMIM, and DrugBank. Potential therapeutic targets were identified by intersecting drug and disease targets, followed by protein-protein interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and MD simulations were performed to evaluate interactions between potential active components and key targets. In vitro experiments were conducted on Aβ 25-35 -induced BV2 microglial cells to assess cell viability (CCK-8 assay), inflammatory cytokine levels (ELISA), and protein expression (Western blot) related to the neuroinflammation pathway and microglial polarization. Results A total of 28 active components and 50 common targets of CCPI for AD treatment were identified. Linoleic acid (LA) was determined to be a potential active component, with IL-6 as the key target based on PPI network topology. Molecular docking and MD simulation confirmed a stable binding affinity between LA and IL-6. KEGG analysis revealed significant enrichment in the HIF-1 signaling pathway, particularly the IL-6/STAT3/VEGF signaling pathway. In vitro , CCPI treatment significantly enhanced cell viability and attenuated the pro-inflammatory response, as evidenced by reduced levels of IL-6, IL-1β, and TNF-α, decreased the expression of the pro-inflammatory marker iNOS. Concurrently, it elevated the expression of the anti-inflammatory/repair-associated marker CD206. Western blot analysis further verified that CCPI suppressed IL-6/STAT3 activation while upregulating VEGF expression. Additionally, LA alone significantly reduced IL-6 levels and STAT3 phosphorylation, decreased the expression of iNOS, and increased the expression of CD206, with therapeutic efficacy comparable to CCPI. Conclusion CCPI exerts neuroprotective effects in AD models by regulating the IL-6/STAT3/VEGF pathway, downregulating the expression of the inflammation-related iNOS protein, upregulating the expression of the CD206 protein associated with anti-inflammatory and reparative functions, remodeling the functional state of microglia, inhibiting their pro-inflammatory responses, and enhancing their reparative functions. Its potential active component, LA, likely mediates this effect by stably binding to and inhibiting IL-6, thus suppressing the downstream STAT3 phosphorylation that drives inflammatory activation.

A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 10 4 M -1 . Fluorescence spectroscopic analysis derived a value of 1.7 × 10 5 M -1 . At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

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Quick Reads

Integration of Network Pharmacology, Molecular Docking, and Molecular Dynamics to Decipher the Multi-Target Mechanisms of Crocin I in Parkinson’s Disease with In Vivo Validation

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Global analysis of thermal and chemical denaturation using CheMelt: Thermodynamic dissection of highly thermostable de novo designed proteins

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Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

Resources & Tools

• Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
• Dataset: PDB-REDO - Optimized protein structure database with refined models.
• Tool: AlphaFold2 - Deep learning system for high-accuracy protein structure prediction. View all tools →
• Tool: ColabFold - Fast AlphaFold2/MMseqs2 pipeline for large-scale predictions. View all tools →
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Event: Structural Biology Events (Open)
• Job: Job Application for Scientist II - Structural Biology at Nurix - Greenhouse at Greenhouse
• Job: Job Application for Scientist / Senior Scientist, Computational Biology at Altos Labs - Greenhouse at Greenhouse

The protein structure is the language of life; design is its poetry. — Recep Adiyaman