George Dock: excerpts from the 34th Dock lecture.
Explore the source record for details and available documents.
History, 19th Century↗
SEARCH · PubMed Health
Results for “Docking”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Viscoelastic-Assisted Patient Interface Docking: A Technical Optimization in LenSx Femtosecond Laser-Assisted Cataract Surgery.
PURPOSE: To evaluate the efficacy of viscoelastic-assisted patient interface docking in LenSx (Alcon Laboratories, Inc) femtosecond laser-assisted cataract surgery (FLACS). METHODS: This was a randomized controlled trial. Patients undergoing FLACS from January to August 2025 at Aier Eye Hospital of Wuhan University were randomized via a random number table to receive balanced salt solution (BSS) or visoeleastic as the patient interface docking medium. The primary outcome was docking efficiency, measured by one-time docking success rate, the number of docking attempts, and mean docking time. Secondary outcomes included surgical safety (subconjunctival hemorrhage, capsulotomy completeness/tear rate), laser treatment duration (anterior capsulotomy time, nucleus pretreatment time, total laser emission time), and patient comfort (post-laser pain sensation). RESULTS: A total of 100 patients were enrolled, 50 in each group. Suction loss occurred in 7 patients (14%) in the BSS group and 1 patient (2%) in the viscoelastic group; the one-time docking success rate was significantly higher in the viscoelastic group (98%) than in the BSS group (86%) (chi-square = 3.93, P < .05). The viscoelastic group also had fewer mean docking attempts (1.02 ± 0.14) than the BSS group (1.16 ± 0.42), showing a significant difference (t = 2.23, P < .05). The viscoelastic group exhibited significantly shorter mean docking time (44.66 ± 4.47 seconds) compared to the BSS group (48.62 ± 3.11 seconds) (t = 2.17, P < .05). No significant differences were observed between the groups in subconjunctival hemorrhage, capsulotomy completeness/tear rate, anterior capsulotomy time, nucleus pretreatment time, total femtosecond laser emission time, or patient-reported pain sensation (all P > .05). CONCLUSIONS: Viscoelastic-assisted patient interface docking in FLACS effectively elevates one-time docking success rate, reduces docking attempts, and shortens docking time.
Humans↗
New algorithm to model protein-protein recognition based on surface complementarity. Applications to antibody-antigen docking.
A novel algorithm is presented which models protein-protein interactions using surface complementarity. The method is applied to antibody-antigen docking. A steric scoring scheme, based upon a soft potential, is used to assess complementarity, and a simple electrostatic model is then used to remove infeasible interactions. The soft potential allows for structural changes that occur during docking. Biochemical knowledge is necessary to reduce the number of docking orientations produced by the method to a manageable size. The information used includes the known epitope residues and a single loose distance constraint. The method is applied to all three crystallographically determined antibody-lysozyme complexes, HyHEL-10, D1.3 and HyHEL-5. For the first time, a predicted antibody structure (that of D1.3) is used as a docking target. In the four systems modelled, the method identifies between 15 and 40 possible docking orientations. The root-mean-square (r.m.s.) deviation between these orientations and the relevant crystallographic complex is measured in the interface region. For all four complexes an orientation is found with r.m.s. deviation in the range 1.9 A and 4.8 A. The algorithm is implemented on a single instruction/multiple datastream (SI/MD) architecture computer. The use of a parallel architecture computer ensures detailed coverage of the search space, whilst still maintaining a search time of two days.
Algorithms↗
Investigating the molecular mechanism of Yangxin decoction in treating major depressive disorder using network pharmacology and molecular docking technology approaches.
Yangxin decoction has been used to treat major depressive disorder (MDD). This study aims to identify the active components and potential mechanisms of Yangxin decoction in treating MDD using network pharmacology and molecular docking technology. The active components and targets of Yangxin decoction were screened, and MDD-related targets were predicted. Networks of "herbal medicine-active components-potential targets" and protein-protein interaction were constructed. Core components and core targets were identified through network topology analysis. Gene ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on candidate genes. Molecular docking was conducted using AutoDock software (Olson Laboratory of the Scripps Research Institute, San Diego) to explore the interactions between core targets and active components, and the results were visualized using PyMOL (DeLano Scientific LLC, South San Francisco). A total of 433 active components and 392 targets of Yangxin decoction were identified, along with 11,796 MDD-related targets. There were 680 overlapping targets between Yangxin decoction and MDD, associated with 104 active components. Core targets identified through network topology analysis and molecular docking included serine/threonine kinase 1 (AKT1), tumor necrosis factor, interleukin-6, tumor protein P53, and proto-oncogene tyrosine-protein kinase Src. Gene ontology enrichment analysis revealed 1606 biological processes, 191 cellular components, and 373 molecular functions. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified 212 signaling pathways, with significant enrichment in caffeine metabolism, bladder cancer, advanced glycation end products-receptor for advanced glycation end products signaling pathway in diabetic complications, and vascular endothelial growth factor signaling pathway. Molecular docking results showed strong binding energy between core active components and core targets. Yangxin decoction exhibits multi-component, multi-pathway, and multi-target therapeutic characteristics. It primarily regulates targets such as AKT1, tumor necrosis factor, interleukin-6, tumor protein P53, and proto-oncogene tyrosine-protein kinase Src through advanced glycation end products-receptor for advanced glycation end products, vascular endothelial growth factor, and ErbB signaling pathways, exerting anti-inflammatory, immune-regulating, and oxidative stress-inhibiting effects to alleviate MDD.
Molecular Docking Simulation↗
Analysis of the molecular mechanism underlying di(2-ethylhexyl) phthalate-induced bladder carcinogenesis via network toxicology and molecular docking approaches: An observational study.
This study aims to investigate the toxicity of di(2-ethylhexyl) phthalate (DEHP) and the potential molecular mechanisms of DEHP-induced bladder cancer (BLCA) using network toxicology and molecular docking strategies. The toxicity of DEHP was assessed using Prox-II software, and potential targets for DEHP-induced BLCA were identified by integrating data from ChEMBL database, Search Tool for Interactions of Chemicals, SwissTargetPrediction, GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, and The Cancer Genome Atlas. STRING database and Cytoscape were employed to construct target networks and determine core targets. The expression levels of core targets were analyzed using R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on potential and core targets. Molecular docking was carried out using CB-Dock 2 to verify the interactions between DEHP and core targets. A total of 105 potential targets related to DEHP-induced BLCA were identified, from which 7 core targets were selected: cyclin-dependent kinase 1, interleukin 6, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, cyclin B2, and B-cell lymphoma 2. IL-6 and B-cell lymphoma 2 showed downregulated expression in tumor tissues, while cyclin-dependent kinase 1, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, and cyclin B2 were upregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these targets were enriched in cell signaling and cancer-related pathways. Molecular docking confirmed that DEHP interacts with these core targets. DEHP may promote the development of BLCA by interacting with key proteins and signaling pathways. This study provides a theoretical basis for understanding the molecular mechanisms of DEHP-induced BLCA and offers references for future prevention and treatment strategies.
Diethylhexyl Phthalate↗
Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.
INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.
Molecular Docking Simulation↗
A Network Pharmacology and Molecular Docking Study of TongBi Formula for Osteoarthritis.
This study applied network pharmacology combined with molecular docking to predict the potential therapeutic targets and molecular mechanisms of TongBi Formula (TBF) in osteoarthritis (OA). Active components and corresponding targets of TBF were retrieved from the traditional Chinese medicine Systems Pharmacology Database and Analysis Platform, while OA-related targets were collected from Online Mendelian Inheritance in Man, GeneCards, DrugBank, and Therapeutic Target Database. A network visualization and analysis software was used to construct compound-target and protein-protein interaction (PPI) networks. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery platform. Molecular docking analysis was conducted using a molecular docking software to evaluate the predicted binding affinity between key active compounds and core target proteins. A total of 47 overlapping targets between TBF and OA were identified. PPI network analysis highlighted JUN, RELA, IL6, MAPK1, and IL10 as potential hub targets. Enrichment analysis suggested that TBF may regulate inflammation, lipid metabolism, and multiple intracellular signaling pathways associated with OA progression. Molecular docking results demonstrated favorable predicted binding affinities between core active compounds and key OA-related protein targets. These findings provide a computational framework for understanding the potential mechanisms of TBF against OA and support further experimental validation.
Molecular Docking Simulation↗
Integrated Network Pharmacology and Molecular Docking Analysis of Sishen Decoction Identifies Potential Targets and Pathways in Gout.
Gout is a disease characterized by hyperuricemia and the deposition of urate crystals in joints and soft tissues, leading to recurrent acute arthritis. Its increasing prevalence imposes substantial clinical and socioeconomic burdens. Sishen Decoction (SSD) has been used in the treatment of gout, but its potential molecular mechanisms remain unclear. This study applied an integrated network pharmacology and molecular docking approach to identify potential targets and signaling pathways associated with SSD in gout. Active compounds and corresponding targets of SSD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), while gout-related targets were collected from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Overlapping targets were identified and used to construct a drug-component-target-disease network. A protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed, followed by molecular docking using the docking server analysis module. A total of 37 bioactive compounds were associated with 116 overlapping gout-related targets. The top hub targets included TP53, IL6, IL1B, TNF, AKT1, EGFR, CASP3, JUN, BCL2, and MMP9. GO analysis suggested that these targets are involved in gene expression regulation and signal transduction. KEGG enrichment analysis indicated significant associations with the mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), interleukin-17 (IL-17), and tumor necrosis factor (TNF) signaling pathways. Molecular docking predicted favorable interactions between key compounds and hub targets, with all binding energies of ≤-5 kcal/mol. These computational findings provide potential mechanistic hypotheses for the action of SSD in gout and may support future experimental validation.
Molecular Docking Simulation↗
Docking by least-squares fitting of molecular surface patterns.
Molecular surfaces are fitted to each other by a new solution to the problem of docking a ligand into the active site of a protein molecule. The procedure constructs patterns of points on the surfaces and superimposes them upon each other using a least-squares best-fit algorithm. This brings the surfaces into contact and provides a direct measure of their local complementarity. The search over the ligand surface produces a large number of dockings, of which a small fraction having the best complementarity and the least steric hindrance are evaluated for electrostatic interaction energy. When applied to molecules taken from crystallographically observed complexes, this procedure consistently assigns the lowest electrostatic energies to correct dockings. On independently determined structures, the ability of the method to discern correct dockings depends on how much conformational difference there is between the free and complexed forms of the molecules. The procedure is found to be fast enough on contemporary workstation computers to permit many conformations to be considered, and tolerant enough to make rather coarse bond dihedral sampling a practicable way to overcome the problem of structural flexibility.
Binding Sites↗
Integrated network pharmacology, molecular docking and experimental validation to investigate the mechanism of tannic acid in nasopharyngeal cancer.
Tannic acid (TA) is the primary bioactive component in the gallnut (Galla chinensis) and has exhibited the anticancer effects. However, the mechanism of its anti-cancer activity in nasopharyngeal carcinoma (NPC) remains unclear. This research aims to explore the underlying mechanism of TA in the treatment of nasopharyngeal cancer using network pharmacology, molecular docking and experimental validation. Firstly, the targets of TA and NPC were predicted and collected through databases, and the intersection targets were identified. Subsequently, protein-protein interaction (PPI) network analysis, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment analysis, molecular docking and molecular dynamics (MD) simulation were conducted to uncover the potential mechanisms of TA in treatment of NPC. Finally, in vitro experiments were utilized to verify the mechanism of TA with anticancer activity in NPC. The results of network pharmacology revealed 42 intersection targets between NPC-related targets and TA-related targets. The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling was identified as the main target pathway of TA against NPC. Additionally, molecular docking and MD simulation confirmed the closely binding affinities of TA with AKT1. Furthermore, the results of in vitro experiments demonstrated that TA exerts anticancer activity against NPC by targeting the PI3K/AKT signaling pathway, leading to the suppression of cell proliferation. TA is a promising therapeutic candidate for NPC through PI3K/AKT signaling pathway. These results provide insights into the clinical application of TA, particularly when considered in combination with other therapeutic modalities.
Molecular Docking Simulation↗
Network pharmacology and molecular docking to explore the active compounds and mechanisms of Jerusalem artichoke for treating diabetes.
The effective components and mechanism of Jerusalem artichokes (JAs) in lowering blood glucose were studied through network pharmacology and molecular docking. The active compounds of Jerusalem artichoke were obtained by referring to the literature, and the active compounds were screened. The targets were predicted by the SwissTargetPrediction database, and the disease targets were screened using GeneCard, Disgenet, and OMIM databases. The protein-protein interaction (PPI) network diagram was constructed using the STRING database, and the intersection target was analyzed by gene ontology (GO) biological function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the David database. Finally, molecular docking was verified using AutoDockTools1.5.7 software. After screening, 412 gene targets, 476 disease targets, and 64 intersection targets were identified. The results of GO biological function analysis and KEGG pathway analysis showed that the technology was involved in multiple biological processes and regulatory pathways for hypoglycemia, such as the HIF-1, PI3K-Akt, and AMPK signaling pathways. Molecular docking results showed that Jasmonate, Liquiritigenin and Inulin of JAs had strong binding effects with PPARG and STAT3. JAs exert hypoglycemic effects through multi-component, multi-target and multi-pathway. In summary, this study investigated the hypoglycemic mechanism of JAs using network pharmacology and molecular interconnection technology, and concluded that JAs exert hypoglycemic effects through multiple components, targets, and pathways, which provides a theoretical basis for the study of JAs.
Molecular Docking Simulation↗
Molecular mechanism of HaiZao-YuHu decoction in breast cancer treatment via network pharmacology and molecular docking: Computational pharmacology.
BACKGROUND: The molecular biological mechanisms of HaiZao-YuHu decoction were investigated using network pharmacology and molecular docking. METHODS: TCMSP database was used to collect the active ingredients and action targets of HaiZao-YuHu decoction, through the OMIM, PharmGkb, GeneCards, TDD, and DurgBank database query targets for breast cancer. Then, using the intersecting targets, the protein-protein interaction network of HaiZao-YuHu decoction was constructed using the STRING website. Network topology analysis was performed using Cytoscape 3.9.0 to identify the core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed with the R package. The Autodock software was used for molecular docking. RESULTS: Thirty-four active ingredients, 219 intersection targets and 4 key targets were obtained. gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis get 2152 biological processes and 186 pathways. Molecular docking showed that the 4 core targets could combine well with the 5 main active components. CONCLUSION: HaiZao-YuHu decoction can play a role in the treatment of breast cancer through multi-targets, multi-components, and multi-pathways.
Molecular Docking Simulation↗
Elucidating the mechanism of Buyang Huanwu Decoction in the treatment of ischemic stroke: A network pharmacology and molecular docking study.
A large number of functional disorders and uncomfortable symptoms often remain following ischemic stroke (IS). Existing drug therapy is not ideal for the direct improvement of symptoms, which often leads to poor patient compliance with physical rehabilitation therapy. Buyang Huanwu Decoction (BYHWD) is a famous prescription that is effective in treating IS, especially during the sequela stage of IS. We analyzed the therapeutic mechanism of BYHWD through network pharmacology. This study aims to investigate the potential active ingredients, targets, and signaling pathways of BYHWD for the treatment of IS, utilizing network pharmacology and molecular docking technology. The active ingredients of 7 Chinese herbs in BYHWD were obtained from the Traditional Chinese Medicine Systems Pharmacology and HERB databases, and IS-related disease targets were searched in the DisGeNET, GeneCards, and OMIM databases. The protein-protein interaction network was constructed using the STRING database and analyzed by Cytoscape 3.10.2 software. Additionally, the target genes were uploaded to the Database for Annotation, Visualization, and Integrated Discovery website for Gene Ontology alongside Kyoto Encyclopedia of Genes and Genomes analyses. With the assistance of AutoDockTools and PyMOL software (Schrödinger, Inc.), a validation of molecular docking results and a visualization of the results were performed. The results showed that there were 190 intersection targets between the active drug components and IS, corresponding to 61 active components, among which the top 5 target genes were tumor suppressor protein 53, Jun proto-oncogene, AKT serine/threonine kinase 1, mitogen-activated protein kinase 1, and estrogen receptor alpha. The PI3K-Akt signaling pathway is one of the top 10 pathways. The molecular docking results indicated that most of the top 5 targets had good affinities for the 8 core compounds. This computational analysis suggests that BYHWD may treat IS through multiple targets and pathways. It may play a neuroprotective role by regulating the inflammatory response, oxidative stress, apoptosis, autophagy, and vascular endothelial homeostasis. The identification of core effective components provides a theoretical foundation and candidate compounds for further investigation into new drugs for the treatment of sequelae after IS.
Drugs, Chinese Herbal↗
Exploring the treatment of liver cancer with Gehua Hugan Gao based on bioinformatics, network pharmacology, and molecular docking.
Gehua Hugan Gao (GHHGG) is a traditional Chinese medicine paste that is chiefly used to treat liver cancer. However, the potential impact of GHHGG on liver cancer remains unclear. We explored how GHHGG treats liver cancer using bioinformatics, network pharmacology, and molecular docking. Network pharmacology included GHHGG active ingredients, predicted targets, predicted targets for liver cancer, and differential gene collection. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and crucial targets were ranked according to their degree values. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of liver cancer targets were followed by survival, differential analysis, and molecular docking. Venn diagrams show 123 predicted GHHGG targets for the treatment of hepatocellular carcinoma (HCC). Enrichment analysis showed that GHHGG treats HCC through multiple targets and pathways. We also found that estrogen receptor 1, cytochrome P450 3A4, cyclin-dependent kinase 4, type IIA topoisomerase, aurora kinase A, and cyclin E1 targets were closely associated with HCC development through survival and differential analyses. Molecular docking confirmed GHHGG's strong affinity for liver cancer targets. This study helps us understand GHHGG ingredients and targets for liver cancer treatment. To a certain extent, the molecular mechanism of GHHGG in the treatment of liver cancer has been elucidated, thus providing a theoretical basis.
Molecular Docking Simulation↗
Validating the potential mechanism and therapeutic effect of Qinlian Jiangxia decoction in the treatment of type 2 diabetes mellitus complicated with hyperlipidemia through network pharmacology, molecular docking, molecular dynamics simulation, andexperiments.
OBJECTIVE: To investigate the mechanism of action of Qinlian Jiangxia decoction (, QLJXD) in the treatment of type 2 diabetes mellitus (T2DM) complicated by hyperlipidemia using network pharmacology, molecular docking, molecular dynamics simulation and in vivo experiments. METHODS: Drug components, targets and disease targets were identified using databases such as TCM systems pharmacology database and analysis platform and GeneCards. The intersecting targets were subjected to protein-protein interaction analysis using the search tool for the retrieval of interacting genes/proteins database. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the intersecting targets were conducted using the Metascape platform to identify core components and targets. The results were validated using molecular docking, molecular dynamics simulations and in vivo experiments. RESULTS: QLJXD contains 76 active ingredients and 136 disease targets. The core ingredients are quercetin, β-sitosterol, wogonin and baicalein, while the core targets are fatty acid binding protein 4 (FABP4) and peroxisome proliferative activated receptor gamma (PPARG). Molecular docking and molecular dynamics simulations revealed that the core ingredients bound well to the core targets. Animal experiments demonstrated that QLJXD effectively inhibited the expression of FABP4 and increased the expression of PPARG, thereby enhancing disorders of glycolipid metabolism. CONCLUSION: The putative therapeutic efficacy of QLJXD in the management of T2DM complicated with hyperlipidemia may be ascribed to the synergistic actions of multiple components, such as quercetin, β-sitosterol, wogonin, and baicalein, which collectively modulate FABP4 and PPARG molecular targets.
Molecular Docking Simulation↗
Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.
BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.
Molecular Docking Simulation↗
Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.
PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, this study aimed to clarify their association, identify the toxic targets of DEHP, and elucidate the underlying molecular mechanisms. METHODS: Multiple integrated approaches were employed, including Gene Expression Omnibus (GEO) data analysis, network toxicology, molecular docking, and machine learning. STRING and Cytoscape tools were utilized to identify key targets, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functional enrichment of intersecting targets. Machine learning and SHAP analysis were applied to screen core targets in GC. Molecular docking was performed to evaluate the binding affinity of DEHP toward core targets, and 200 ns molecular dynamics simulations were further conducted for representative complexes to validate their dynamic stability. RESULTS: A total of 18 key targets were identified using STRING and Cytoscape. GO and KEGG enrichment analyses demonstrated that these intersecting targets were primarily enriched in the extracellular region, as well as the Calcium signaling pathway and cAMP signaling pathway. Through machine learning analyses, 7 key genes (ADRB2, ESRRG, GRIA4, IL13RA2, NR3C2, PLA2G1B, and SULT2A1) were identified as core targets in GC through machine learning analyses. Molecular docking simulations revealed strong binding specificity between DEHP and the target proteins. Among them, NR3C2 and ADRB2 exhibited relatively high predictive importance in the machine learning models. DEHP showed favorable binding affinity toward these core targets, and molecular dynamics simulations further confirmed that ADRB2-DEHP and NR3C2-DEHP complexes maintained stable conformations throughout the simulation. CONCLUSIONS: Our findings identified GC associated genes that were computationally predicted as potential targets of DEHP. These results indicated structural compatibility between DEHP and its target proteins but did not prove that DEHP exposure accounts for the gene expression changes in GC.
Molecular Docking Simulation↗
Exploring the mechanism of the Lianshi Jianpi formula in treating impaired glucose tolerance: a network pharmacology, molecular docking, and experimental validation study.
OBJECTIVE: To explore the bioactive constituents, key targets, signalling pathways, and molecular mechanisms of Lianshi Jianpi formula (, LSJPF) in the treatment of impaired glucose tolerance (IGT) through network pharmacology, molecular docking, and in vivo experiments. METHODS: The active ingredients and targets of LSJPF were identified using the Traditional Chinese Medicine Systems Pharmacology and HERB databases, whereas the IGT-related targets were sourced from GeneCards, DisGeNET, and PubMed. The overlap analysis identified potential targets of LSJPF. Protein-protein interaction networks and core targets were evaluated using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape, and molecular docking confirmed the binding affinities. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using Metascape. The therapeutic mechanisms were validated in an animal IGT model. RESULTS: LSJPF contained 229 compounds, with 15 active compounds and 77 potential target proteins. The phosphatidylinositol-3-kinase (PI3K)-protein kinase B (AKT) signalling pathway emerged as a key IGT pathway. The KEGG enrichment analysis revealed the pivotal genes RAC-alpha serine/threonine-protein kinase (AKT1), heat shock protein 90 kDa alpha B1, and B-cell lymphoma 2 family protein, which predominantly interact with beta-sitosterol and beta-carotene, the major constituents of Semen Euryales, Semen lablab Album, Semen sojae Atricolor in LSJPF. Molecular docking revealed strong binding affinities between LSJPF and IGT-related targets. In an animal IGT model, LSJPF treatment prevented weight loss; reduced food and water intake; decreased blood glucose levels; improved insulin resistance; decreased serum triglyceride, cholesterol, and low-density lipoprotein cholesterol levels; alleviated liver pathology; and significantly increased the levels of phosphorylated adenosine 5'-monophosphate-activated protein kinase (AMPK), PI3K, and AKT, suggesting its potential role in regulating glucose and lipid metabolism. CONCLUSIONS: These findings reveal the potential of LSJPF as an IGT intervention that targets the AMPK/PI3K/AKT cascade, validating network pharmacology predictions and highlighting the role of multipathway mechanisms in metabolic diseases.
Molecular Docking Simulation↗