Recep Adiyaman
bioinformatics

Issue #29: Mechanistic Investigation of Astragalus Root in the Management of T2DM-NAFLD Comorbidity: An Integrated Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Study

January 23, 2026 Daily Intelligence
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Mechanistic Investigation of Astragalus Root in the Management of T2DM-NAFLD Comorbidity: An Integrated Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Study

šŸ§¬ Abstract

Background: /Objectives: Astragalus root is a classical qi-tonifying traditional Chinese medicine that has demonstrated potential therapeutic efficacy in T2DM and NAFLD. However, the precise mechanisms underlying its effects on the comorbidity of these two disorders remain unclear. This study investigated the molecular mechanisms by which astragalus root ameliorated T2DM-NAFLD comorbidity. Methods: Network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiments were employed to elucidate the potential roles and mechanisms of astragalus root in the management of T2DM-NAFLD comorbidity. Results: A total of 25 bioactive constituents and 152 corresponding targets associated with astragalus root were identified. PPI network analysis revealed the top ten core candidate targets, among which six possessed suitable crystal structures for molecular docking, including IL-6, AKT1, JUN, TNF, CASP3, and ESR1. KEGG analysis further identified the PI3K-AKT as the most significantly en-riched pathway. Molecular docking of the principal bioactive constituent formononetin from astragalus root with the six core targets was conducted using AutoDock4 software. Molecular dynamics simulations verified the stability of the interactions between for-mononetin and each of the six core target proteins. In vitro experiments demonstrated that formononetin obviously decreased lipid droplet accumulation, downregulated TC and TG levels, suppressed the expression of TNF-Ī±, IL-6, and IL-1Ī², decreased ROS and MDA levels, and enhanced GSH content and SOD activity. These therapeutical effects were achieved through inhibition of protein expression within the PI3K/AKT/mTOR signaling pathway. Conclusions: This study determined the potential therapeutic targets and underlying mechanisms of formononetin derived from astragalus root in the T2DM-NAFLD management, thereby providing a scientific basis for its clinical application.

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

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Unraveling the mechanisms of nicotine-induced osteoporosis via network toxicology, bioinformatics, and molecular docking.

Osteoporosis (OP) is linked to smoking. Nicotine may disrupt bone homeostasis through various pathways, but its molecular mechanisms are unclear. This study aims to explore the molecular networks and key regulatory factors underlying nicotine-induced OP. Nicotine toxicity was assessed via ProTox-3.0, with its Simplified Molecular Input Line Entry System (SMILES) structure retrieved from PubChem. Potential targets were predicted using five databases, including SuperPred. OP-related gene data (GSE56815) were extracted from Gene Expression Omnibus (GEO) and combined with GeneCards and Comparative Toxicogenomics Database (CTD) for target screening. Overlapping genes were identified by Venn diagram analysis, followed by protein-protein interaction (PPI) network construction. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using HipLot, while Hallmark Gene Sets provided insights into key biological pathways. Core targets were screened via Cytoscape 3.9.1, and molecular docking was conducted using AutoDockTools 1.5.7. In all, 388 nicotine-associated targets and 1777 OP genes were predicted, with 116 overlapping. Enrichment analyses revealed associations with multiple signaling pathways, particularly those involving apoptosis and estrogen. Eight core targets, including SRC, BCL2, and CASP3, were identified. Molecular docking showed strong binding affinity (approximately -5 kcal/mol), with enhanced binding stability through hydrophobic interactions and hydrogen bonding. This study suggests nicotine exacerbates OP by regulating key targets, such as CASP3 and ESR1, and pathways like apoptosis and estrogen signaling. These findings provide insights into the molecular mechanisms underlying nicotine’s role in OP and potential therapeutic targets.

In Silico Investigation Reveals <i>IL-6</i> as a Key Target of Asiatic Acid in Osteoporosis: Insights from Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Background/objectives Osteoporosis is a multifactorial skeletal disorder in which chronic inflammation, dysregulated cytokine signaling, and metabolic imbalance contribute to excessive bone resorption and impaired bone formation. Asiatic acid has demonstrated bone-protective effects, but its molecular mechanisms in osteoporosis remain incompletely understood. This study aimed to investigate the anti-osteoporotic mechanisms of asiatic acid using an integrative in silico strategy. Methods Network pharmacology analysis was performed to identify osteoporosis-related molecular targets of asiatic acid. Molecular docking was used to predict the binding modes and affinities between asiatic acid and its target proteins. Molecular dynamics simulation was used to assess the structural stability and interaction persistence of the asiatic acid-protein complex. Results Network pharmacology identified 135 overlapping targets between asiatic acid and osteoporosis, with IL-6 , STAT3 , PPARG , and NFKB1 emerging as key hubs. KEGG analysis indicated the PPAR signaling pathway as a potential mechanism underlying the anti-osteoporotic effect. Molecular docking showed strong binding energies of asiatic acid with all predicted target proteins, with the highest affinity observed for IL-6 , involving key residues ASN61, LEU62, GLU172, LYS66, and ARG168. Consistently, molecular dynamics simulation confirmed stable binding of asiatic acid to IL-6 , with persistent interactions with ASN61, LYS66, LEU62, LEU64, and GLN154 mediated by hydrogen bonds, water bridges, and hydrophobic interactions. Conclusions This integrative in silico study provides mechanistic insight into the potential anti-osteoporotic actions of asiatic acid, implicating IL-6 as a plausible upstream molecular target. These results establish a robust mechanistic framework for future translational studies exploring asiatic acid as a natural therapeutic candidate for osteoporosis.

An evaluation of Roluperidone as a promising repurposing candidate for Alzheimer’s Disease: A Computational Investigation.

Alzheimer’s disease (AD) is the most dominant and prevalent form of dementia. The therapeutic agents for AD are not sufficient. Drug repurposing (i.e., also called drug repositioning or therapeutic switching of drugs) could contribute to adding novel therapeutic agents in AD discovery pipeline. Blood-brain barrier (BBB) is a crucial factor, for brain’s diseases related drug discovery. Since, CNS active compounds have BBB crossing property, in this study this category of compounds was re-evaluated as repurposing potential candidate for AD by integrated machine learning algorithm, cheminformatics analysis, molecular Docking and simulation-based approach. We builded three machine learning model such as Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) for the prediction of AD potential repurposing candidates. The SVM classification model performed better than others. The SVM classification model achieved an Area Under the Curve of the Receiver Operating Characteristics (ROC-AUC) of 0.81, along with higher precision, recall, and F1 scores. The support vector machine (SVM) was implemented to classify 500 CNS active compounds as AD drug potential and non-AD drug potential. Using the SVM model, 60 compounds were predicted as AD repurposing potential from 500 CNS active compounds. Structural similarity analysis of 60 compounds with Donepezil as a reference drug was performed using 5 different types of fingerprints such as ‘substructure’, ’extended’, ‘circular’, ‘EState’, ‘MACCS’. 9 compounds from them obtained as structurally most similar to the reference drug. After the molecular docking performance of 9 compounds into the active site & peripheral anionic site of human acetylcholinesterase (hAChE), it was revealed that Roluperidone’ had binding affinity of -12 kcal/mol, and ‘Napitane’ had binding affinity of -11.9 kcal/mol whereas the reference drug Donepezil had a binding affinity of -11.8 Kcal/mol. Molecular dynamics simulation revealed that Roluperionde had better binding integrity to hAChE. This study laid out computational reinvestigation of 500 CNS active drugs for therapeutic switching to AD, and ‘Roluperidone’ is found as an AD repurposing potential candidate. However, in-vitro and in-vivo studies are further needed to fully elucidate the compound’s potential as AD repurposing drugs.

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Targeting Colorectal Cancer With Helicteres isora L.: Integrative Approach Combining Network Pharmacology, Molecular Docking, Molecular Dynamics Simulations, and In Vitro Validation.

Helicteres isora L., a medicinal plant valued in traditional systems like Ayurveda and Chinese medicine, demonstrates significant therapeutic potential, particularly in colorectal cancer (CRC). This study evaluated its antioxidant and anticancer properties using 2,2-diphenyl-1-picrylhydrazyl and 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide assays, respectively. The methanolic fruit extract showed strong antioxidant capacity and moderate cytotoxicity against HCT-116 cells (half-maximal inhibitory concentration: 13.58 Ā± 1.12 ĀµM). Systems biology approaches identified key oncogenic targets-ERBB2, epidermal growth factor receptor, MET, KRAS, TP53, and MAPK1-through network pharmacology and proteinā€’protein interaction analysis. Enrichment analysis linked its phytoconstituents with PI3K-Akt and MAPK pathways. Rosmarinic acid exhibited strong MAPK1 binding (ā€’8.754 kcal/mol), interacting with MET106, ASP109, and LYS52, with molecular dynamics simulations confirming stability. Survival analysis indicated that higher ERBB2 and MAPK1 expression correlated with improved outcomes, while receiver operating characteristic curves revealed moderate predictive value for chemotherapy response. These findings highlight H. isora as a promising source of anticancer agents, particularly targeting MAPK1, and support further exploration for CRC therapy.

Superwater as a generative AI framework to predict water molecule positions on protein structures.

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy. SuperWater surpasses existing methods, delivering state-of-the-art performance in both crystal water coverage and prediction precision, achieving water localization within 0.3 Ā± 0.06 ƅ of experimentally validated positions. We demonstrate the capabilities of SuperWater through case studies involving protein hydration, protein-ligand binding, and protein-protein binding sites. This framework can be adapted for various applications, including structural biology, binding site prediction, multi-body docking, and water-mediated drug design.

Integrated network toxicology, molecular docking, and molecular dynamics simulation reveals mechanisms of benzo[a]pyrene-induced pan-cancer.

Spectrochemical, medicinal, and toxicological studies of moxifloxacin and its novel analogs: a quantum chemistry and drug discovery approach.

Moxifloxacin (MOX) is regarded as a fourth-generation fluoroquinolone, demonstrating effectiveness against multidrug-resistant tuberculosis (TB) by inhibiting bacterial DNA gyrase. The therapeutic effectiveness of MOX is negatively influenced by side effects that are dependent on dosage, including heart rate-corrected QT interval prolongation and hepatotoxicity. This study explored the physicochemical, spectral, biological, and pharmacokinetic properties of MOX and its analogues. We incorporated various functional groups such as CH 3 , NH 2 , OCF 3 , NHCONH 2 , and Cl into the core MOX framework. The geometry was optimized utilizing density functional theory with the B3LYP/6-31g basis set. We conducted geometrical, thermodynamic, molecular orbital, and electrostatic potential analyses to deepen our understanding of their physical and chemical properties. We have obtained the FT-IR and UV-vis spectra and have established correlations with the observed experimental data. The determination of the HOMO-LUMO gap is essential for assessing the chemical reactivity of MOX and its analogs. The methodology of molecular docking was executed, incorporating MOX and its analogs in connection with the targeted protein (PDB ID 5BS8). ADMET prediction was performed to assess absorption, distribution, metabolism, and toxicity, whereas PASS predictions were carried out to examine biological and toxicological properties. MOX13 exhibited a notable HOMO-LUMO gap (3.61 eV), alongside the highest binding affinity (-8.5 kcal mol -1 ) when compared to all examined analogues. MOX13 exhibits a notably pronounced dipole moment (14.88 debye), alongside an exceptional degree of reactivity. Investigations utilizing molecular dynamics were conducted to assess the stability of receptor-ligand complexes by analyzing RMSD, RMSF, H-bonds, and SASA, suggesting that the ligand would remain bound to its original site.

Special Issue “Role of Molecular Dynamics Simulations and Related Methods in Drug Discovery”.

Molecular dynamics (MD) simulations and related computational methodologies-such as binding free energy calculations and Markov state models-have become indispensable tools in modern drug discovery […].

Aromatherapy with Chrysanthemum morifolium cv. Chuju essential oil alleviates allergic rhinitis by modulating the mTOR-PPARĪ³ signaling cascade.

Conventional treatments for allergic rhinitis (AR), such as oral medications and nasal sprays, can effectively alleviate symptoms but often cause side effects, including potential organ damage and symptom relapse after discontinuation. Aromatherapy, a traditional approach used in respiratory care, offers a potentially safer and non-invasive alternative. This study aimed to investigate the therapeutic effects and molecular mechanisms of Chrysanthemum morifolium cv. Chuju essential oil-based aromatherapy (CJA) against AR using network pharmacology, in vivo experiments, molecular docking, and molecular dynamics simulations. Volatile compounds in Chuju essential oil were identified by gas chromatography-mass spectrometry. Network pharmacology analysis was employed to predict the overlapping targets between volatile constituents- and AR-related genes, followed by the construction of a protein-protein interaction network. Key hub genes were identified using the Molecular Complex Detection clustering algorithm. To validate the results of network pharmacology, an ovalbumin-induced AR mouse model was used to evaluate the therapeutic potential of CJA. Molecular biology assays were further performed to verify key targets. Molecular docking and molecular dynamics simulations were then conducted to investigate the binding affinity and stability between the major volatile compounds and their respective targets. Our results demonstrate that (-)-isolongifolol, acetate and (Z,E)-Ī±-farnesene are the major bioactive volatile constituents of Chuju essential oil. These compounds appear to exert anti-inflammatory effects by regulating the mTOR-PPARĪ³ signaling cascade, thereby alleviating AR symptoms in the mouse model. Collectively, these findings highlight the therapeutic potential of CJA as a promising natural intervention for managing AR.

Exploring phytochemical inhibitors of fatty acid elongase ELOVL6 for targeted treatment of chronic myeloid leukemia: A comprehensive network-based drug discovery approach.

Quiescent leukemic stem cells (LSCs) that persist in the bone marrow microenvironment are responsible for chronic myeloid leukemia (CML) relapses and tyrosine kinase inhibitors (TKIs) resistance. This highlights a critical need to uncover alternative gene targets and pathways involved in LSC maintenance. Network biology in drug development has become essential for predicting drug targets in CML disease. This present computational study aims to identify key regulatory genes that are differentially expressed and involved in molecular pathway alternative to BCR-ABL, which may facilitate the eradication of leukemic stem and progenitor cells. Comparative analysis between CML stem and progenitor cells and their normal counterparts revealed 182 differentially expressed genes (DEGs). Applying Weighted Gene Co-expression Network Algorithm (WGCNA) identified a significant gene module comprising 73 hub genes. Protein-protein interaction and enrichment analyses indicated these genes are involved in mitochondrial translation elongation, steroid metabolism, cholesterol, and fatty acyl-CoA biosynthesis. Furthermore, a three-node regulatory network composed of hub genes, CML-associated transcription factors (TFs), and differentially expressed microRNAs (DEMs) was constructed, highlighting three key regulators: ELOVL6, SP1 (TF), and miR-1207-5p. To explore the therapeutic potential of the overexpressed target gene ELOVL6, we performed high-throughput virtual screening of phytochemical compounds against the ELOVL6 protein structure. Subsequent molecular docking, pharmacokinetics, toxicity, and molecular dynamics (MD) simulations revealed two phytochemicals - withaphysalin A and chelidimerine -as potential inhibitors of the ELOVL6 therapeutic biomarker in CML.

Identification of a chromatin-modifying gene-histone lysine N-methyl transferase (KMT2C/MLL3) as a potential immunomodulator oncogene in Indian pancreatic cancer patients.

Background Due to lack of early biomarkers, pancreatic cancer (PanCa), often manifests late, with few treatment options and poor prognosis, Although epigenetic regulators-particularly lysine methyltransferases like KMT2C-are becoming increasingly linked to cancer biology, their function in PanCa is still poorly understood. Objectives This study aims to investigate KMT2C’s mutational and expression landscape in Indian PanCa patients, to explore it’s possible role in carcinogenesis and immune modulation, and to assess its druggability through computational docking and dynamic simulations. Methods Clinical samples from Indian PanCa patients were used for differential expression and mutation studies. Differential expression, methylation, mutation, and immune cell infiltration profiling were also conducted using public databases (TCGA, GEO, CPTAC, and CCLE). Regulatory networks, scRNA-seq analysis, and protein-protein interaction networks were mapped. To evaluate ligand binding to KMT2C, molecular docking and 100-ns molecular dynamics simulations were used. Results KMT2C was overexpressed and exhibited a significantly higher mutational frequency (62.5%) in Indian PanCa samples in contrast with Western cohorts. Its role in immune suppression was implicated by positive correlations observed between KMT2C expression and several. immune-checkpoint receptor expression and regulatory T-cell infiltration. KMT2C was connected by functional enrichment to inositol phosphate metabolism and chromatin remodelling. The therapeutic potential of protodioscin was suggested by its strong binding affinity to KMT2C and the formation of stable interactions confirmed by MD simulations. Conclusion This study suggests KMT2C to be a putative oncogene in Indian PanCa patients in contrast with Western PanCa patients, with immunomodulatory effects and therapeutic potential, implicating its role as a promising biomarker, requiring additional clinical validation, emphasizing the necessity of ethnically informed precision oncology.

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Employ HADDOCK for ambiguous restraints in protein-protein docking.

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

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