Issue #62: Identification of Bioactive Ingredients and Mechanistic Pathways of Xuefu Zhuyu Decoction in Ventricular Remodeling: A Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations.

Signal of the Day

Identification of Bioactive Ingredients and Mechanistic Pathways of Xuefu Zhuyu Decoction in Ventricular Remodeling: A Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations.

Background Xuefu Zhuyu Decoction (XFZYD) is clinically used in China to promote blood circulation, resolve blood stasis, and alleviate ventricular remodeling (VR). However, its molecular mechanisms remain unclear. Objective This study investigates the active components and underlying molecular mechanisms of XFZYD in treating VR. Methods Targets of XFZYD’s active components and VR-related targets were identified. A protein-protein interaction (PPI) network and a drug-ingredient-target network were constructed. GO functional annotation and KEGG pathway enrichment analysis were performed to explore biological functions. Hub targets and their corresponding active ingredients were validated through molecular docking and molecular dynamics (MD) simulations. Results A total of 1,089 active ingredients with high gastrointestinal absorption (GI) and drug-likeness (DL ≥ 2) were identified. Five hundred and thirty-eight common targets were shared between XFZYD and VR, with 10 core targets, including AKT1, STAT3, TP53, EGFR, SRC, TNF, MAPK3, CTNNB1, IL6, and VEGFA. GO analysis revealed XFZYD’s influence on wound healing, oxygen response, epithelial cell proliferation, and receptor signaling. KEGG analysis highlighted key pathways such as PI3K-Akt signaling, lipid and atherosclerosis, and fluid shear stress. Molecular docking revealed that active ingredients display favorable interactions with the hub genes, with binding energies from -9.5 to -6.0 kcal/mol. These interactions were further validated through MD simulations, demonstrating stable binding throughout the 100 ns simulation period. Conclusion XFZYD exhibits therapeutic effects on VR through multiple active components and pathways, providing a scientific basis for its clinical application and further research.

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

Also Worth Reading

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking.

Summary Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand-receptor interactions from scRNA-seq data and performs structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell-cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and implementation scDock is freely available at https://doi.org/10.6084/m9.figshare.31370368 and https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian. Supplementary information Supplementary data are available at Bioinformatics online.

AlphaFold for Docking Screens.

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Exploring the Mechanism of Action of Chicoric Acid Against Influenza Virus Infection Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

This study theoretically explores the mechanism of action of Chicoric acid against influenza virus based on network pharmacology, molecular docking, and molecular dynamics simulation techniques, aiming to provide insights for the development of new veterinary drugs for influenza. Potential targets for influenza virus action were identified using the PharmMapper (i.e. Version 2017) server and disease databases including GeneCards and OMIM. The STRING online analysis platform and Cytoscape 3.9.1 software were employed to construct a protein-protein interaction (PPI) network of the target proteins, followed by topological analysis to screen for key targets. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets using the DAVID database. A “drug-target-pathway” network diagram was constructed using Cytoscape 3.9.1 software. Molecular docking was carried out with AutoDock 1.5.6 and PyMOL 2.5 software to identify dominant binding targets, followed by molecular dynamics simulation analysis. The results of network analysis showed that there were 31 potential targets of Chicoric acid; the protein interaction network suggested that UBC, UBA52, RPS27A, HCK, and CDKN1B may be the core targets of Chicoric acid; 55 cell biological processes were obtained by GO enrichment analysis, and 15 related signaling pathways were obtained by KEGG pathway enrichment analysis; molecular docking showed that UBC and UBA52 had a good affinity to Chicoric acid and may be the dominant target of Chicoric acid exerting its effect. Chicoric acid may play a role in antiviral activity by acting on the dominant protein of UBC and UBA52, thus achieving an anti-influenza virus effect.

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Pipeline Tip

Use local MSA generation (colabfold_search) to bypass speed bottlenecks.

Resources & Tools

• Dataset: SCOPe - Curated structural classification of proteins for fold analysis.
• Dataset: Pfam - Protein families database with curated multiple sequence alignments.
• Tool: ProteinMPNN - High-speed sequence design optimized for fixed-backbone folding. View all tools →
• Tool: OpenFold - Fast, trainable, and open implementation of AlphaFold2. View all tools →
• Event: Structural Biology Events (Open)
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Job: Middle Author Bioinformatics hiring Bioinformatician in Arizona, United States - LinkedIn at Bioinformatics Careers
• Job: vueverse. hiring Bioinformatics Software Engineer (Python) – Remote in India - LinkedIn India at Bioinformatics Careers

Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman