Recep Adiyaman
bioinformatics

Issue #19: Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

January 11, 2026 Daily Intelligence
Protein Design Daily

Building something in Protein Design?

I love collaborating on new architectural challenges. Let's build together.

LinkedIn Connect Direct Message

šŸ§¬ Protein Design Digest

Curated protein signals by Recep Adiyaman

Join 1,000+ researchers. Unsubscribe anytime.

šŸš€ Today’s Top Signal

Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

šŸ§¬ Abstract

Exposure to arsenic (As) is a serious environmental and public health risk because it can cause systemic toxicity, which could lead to serious cardiovascular disease like heart failure, arrhythmias, and coronary heart disease (CHD). Exploring safer and multi-target therapeutic agents is gaining popularity as a result of the shortcomings of traditional therapies. The isoquinoline alkaloid berberine which is derived from plants, exhibits strong anti-inflammatory, antioxidant, and cardioprotective properties. This study employs an integrated network pharmacology and molecular docking approach to investigate the molecular mechanisms and therapeutic potential of berberine in arsenic-induced cardiotoxicity. Key genes target arsenic-induced cardiotoxicity and berberine, have been identified using the Swiss Target Prediction, Gene Cards, OMIM, and CTD databases. A protein-protein interaction (PPI) network was generated by analysing frequently intersecting genes with the STRING and Cytoscape tools. Shiny GO was used to conduct pathway enrichment analysis for the KEGG and Gene Ontology databases. Auto Dock was used to assess berberine’s binding affinity. Berberine and arsenic-related cardiotoxicity shared 17 common targets. The primary targets were identified using Cytoscape ABL-1 (2G2F), CDK2 (1HCK), CYP19A1 (3EQM), ICAM-1 (4G6J), KIT (1T45), MAPK14 (3PY3), PGR (1A28), PTGS2 (5F19), RAC1 (3TH5), and SRC (2SRC). Enrichment analysis revealed TNF, VEGF, and AGE-RAGE signaling involvement, all of which are linked to oxidative stress, inflammation, and endothelial dysfunction. Binding affinity between berberine and the target was found to be ABL-1 (-9.2 kcal/mol), PTGS2 (-8.8 kcal/mol), SRC (-8.7 kcal/mol), CYP19A1 (-8.6 kcal/mol), KIT (-8.3 kcal/mol), RAC1 (-7.9 kcal/mol), CDK2 (-7.5 kcal/mol), ICAM-1 (-7.2 kcal/mol), MAPK (-6.8 kcal/mol), PGR (-5.6 kcal/mol). Berberine has multi-targeted therapeutic potential for arsenic-induced cardiotoxicity by modulating inflammatory and oxidative pathways. These results could support the possible usage of berberine in the treatment of cardiovascular diseases caused by arsenic and provide a mechanistic link for further experimental validation.

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

ā­ Additional Signals

Mechanisms of cellular senescence combined with molecular docking strategies: A biomarker study of potential therapeutic targets for allergic rhinitis.

Bioinformatics and molecular docking methods were used to screen potential biomarkers of cellular senescence in allergic rhinitis (Allergic rhinitis AR), which provided a theoretical basis for revealing the mechanism of AR and exploring new therapeutic approaches. Four AR-related gene chips (GSE19187, GSE43523, GSE44037, and GSE51392) were downloaded from the gene expression database (GEO) for data pooling. Screening differential genes (DEGs) were taken to intersect with cellular senescence-related genes (SRGs) to obtain differential senescence genes (DESRGs). The differential senescence genes were subjected to Gene Ontology Database (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and GSEA enrichment analysis. Protein-protein interaction (PPI) networks were constructed through the STRING database, MCODE plugin weights were analyzed to identify important gene cluster modules, and Hub genes were screened using the CytoHubba plugin topological network algorithm. Hub gene protein interactions network (GeneMANIA) was constructed by the GeneMANIA database. Predict Hub gene construct mRNA-miNA-lncRNA interactions by miRanda, miRDB, miRWalk, TargetScan, and spongeScan databases; construct Hub gene transcription factor regulatory networks by TRRUST database; analyze Hub gene-drug interactions by DGIdb database and select commonly used drugs in the clinic for molecular docking validation. A total of 264 differential genes were screened in the training set with corrected P.adj < 0.05 and |log2FC| ā‰„ 1.2 as the filtering condition, and a total of 866 cellular senescence genes, and 20 differential senescence genes (DESRGs) were obtained by taking the intersection of the two. A total of 19 Hub genes were obtained after PPI analysis, which were CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, AHR The gene-protein interaction network identified 19 Hub genes associated with 21 functional proteins. 5 of the Hub gene loci were associated with 29 miRNAs and 53 lncRNAs. The transcription factor regulatory network obtained 15 transcription factors capable of regulating Hub genes. The analysis of drug-gene interactions identified 489 drugs that target hub genes. For example, in the case of budesonide, the interacting genes STAT1, TLR2, TLR3, and AHR were selected for molecular docking. Similarly, for mometasone, the interacting genes TLR2 and CTH were chosen for molecular docking. Mining AR-related Hub senescence genes by bioinformatics analysis, constructing PPI network, ceRNA network, transcription factor regulatory network, gene-drug interaction network and molecular docking validation, we screened 19 CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, and AHR are expected to be Hub genes for potential diagnostic and therapeutic biomarkers, which will provide targets and new insights for further in-depth explorations of AR cellular senescence-related mechanisms of action and therapy.

Integrating machine learning and molecular docking to elucidate the mechanism of atrial fibrillation induced by di(2-ethylhexyl) phthalate.

Environmental exposure is closely associated with the development of cardiovascular diseases. This study aims to explore the molecular mechanism by which Di (2-ethylhexyl) phthalate (DEHP) induces atrial fibrillation (AF). AF-related target genes were identified through differential expression analysis of multiple datasets. Machine learning algorithms, Weighted Gene Co-expression Network Analysis (WGCNA), Machine learning (ML) and molecular docking technology were integrated to investigate the binding interaction between DEHP and target proteins. A total of 8 potential key targets (ITGB2, ARPC1B, RYR2, FPR2, MPEG1, PRKCD, LCP1, RAC2) involved in DEHP-induced AF were identified. ML analysis confirmed these genes as core regulatory genes, among which ITGB2, ARPC1B, and RYR2 exhibited high diagnostic potential (Area Under the Receiver Operating Characteristic Curve, AUC ā‰„ 0.85). Molecular docking simulations showed stable binding specificity between DEHP and these core targets, with binding energies all below -3 kcal/mol. DEHP may promote AF pathogenesis by targeting specific genes and signaling pathways. DEHP has high binding affinity with ITGB2, ARPC1B, and RYR2, which may serve as targets for future interventions. These findings provide important insights into the in-depth exploration of the mechanism underlying DEHP-induced AF.

Mechanistic study of plastic monomers in gestational diabetes mellitus: A network toxicology and molecular docking approach.

Plastics are widely used in various fields such as food packaging, textile fibers, building materials, and transportation. Although the relationship between plastic additives and diseases has been reported, there is limited research on the association between plastic monomers (PM) and gestational diabetes mellitus (GDM). This study aims to investigate the link between environmental PM and GDM. By employing advanced network toxicology and molecular docking techniques, we successfully elucidated the molecular mechanisms by which PM may induce GDM. Utilizing databases such as PubChem, SEA, Super-PRED, SwissTargetPrediction, PharmMapper, Gene Cards, and OMIM, we identified potential targets associated with the disease. Further analysis using STRING and Cytoscape software helped determine the core targets most significantly related to these metabolic disorders. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the David database to characterize these core targets. Finally, molecular docking with CB-Dock2 was used to validate the binding affinity of PM to these target proteins. Our findings suggest that PM may potentially induce GDM by modulating the insulin signaling pathway through STAT3, AKT1, and TP53. In summary, this work provides novel insights into the mechanisms by which environmental pollutants may trigger GDM, thereby laying a theoretical foundation for disease prevention and treatment. It offers valuable references for the safety evaluation of plastics, urging food safety regulatory agencies to strengthen oversight and encouraging the public to reduce plastic usage.

šŸ§Ŗ AI & Research News

šŸ¢ Industry Insight & Applications

āš” Quick Reads

Green synthesis of 2D azine-linked covalent organic framework with antibacterial activity correlated by molecular docking study and computational calculations.

A two-dimensional covalent organic framework (2D-COF), COFTHB, was synthesized via a Schiff base condensation of terephthaldehyde and 1,4-hydrazonmethylbenzene under green, room-temperature conditions. COFTHB exhibits a mesoporous structure (pore size = 3.68 nm), excellent chemical stability, and high thermal stability up to 629 Ā°C. It demonstrated superior antibacterial activity against both Gram-negative (Pseudomonas aeruginosa, Escherichia coli) and Gram-positive (Enterococcus faecalis, Staphylococcus aureus) bacteria compared to hydrazonmethyl benzene and a model compound (M). Molecular docking simulations revealed the interactions of COFTHB with various proteins, while Density Functional Theory (DFT) (WB97XD/6-311G) analysis of COFTHB, HB (1,4-bis(Z)-hydrazonomethyl benzene), and the model compound provided insights into their electronic properties, reactivity, and resonance effects through Frontier Molecular Orbitals (FMO), Electrostatic Potential (ESP), and Molecular Electrostatic Potential (MEP) analyses. These results suggest COFTHB as a promising platform for antibacterial applications in water treatment.

Exploring Therapeutic and Multitarget Mechanism of Molsidomine on Cerebral Ischemia: Molecular Docking and Multifaceted In Silico Study.

The innate immune response is started immediately following brain ischemia, resulting in adaptive immunity. More and more experimental evidence has indicated that the immune response caused by cerebral ischemia plays a crucial role during the initial brain damage and subsequent brain injury recovery. Molsidomine, a nitric oxide donor, has been found to reduce schizophrenia-like behavioral abnormalities in rats due to glutamate hypofunction. The current study sought to explore the efficacy of molsidomine versus conventional drugs in cerebral ischemia. This study scavenges literature to retrieve various genes like PI3K, MMP9, COX2, LOX, TLR4, AKT1, mTOR, NLRP3, STAT3, IL1Ī², NFĪŗB, TNF, IL6, and TP53 involved in cerebral ischemia. A two-step molecular docking revealed significant gene-drug complexes followed by a protein-protein interaction (PPI). Additionally, oncoprint analysis, protein-protein interaction, and functional and pathway enrichment associated with these genes and a circos plot depicting the mutational profile of the genes are incorporated in this study. This comprehensive in-silico approach reveals the efficacy of the potency of the candidate drug, molsidomine in treating cerebral ischemia.

Protein Language Models: Is Scaling Necessary?

Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide an accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFYs pre-training codebase, data, and model checkpoints.

CHRNA5 D398N mutation in addiction: Pathogenic insights and therapeutic discovery.

Background Drug addiction is a significant public health issue, with genetic components playing a role in predisposing a person to susceptibility. Objective The current study planned to investigate the pathogenic potential of the CHRNA5 D398N (rs16969968) mutation and computationally identify structurally optimized aripiprazole analogs capable of selectively binding the mutant protein. Methods PolyPhen-2 and AlphaMissense were used to assess the pathogenic impact of the D398N (rs16969968) mutation in CHRNA5. SwissSimilarity was employed to identify aripiprazole-like structural analogs for therapeutic screening. The chemical structures of selected analogs were drawn and optimized using ChemDraw. SwissADME was used to evaluate pharmacokinetic properties, including drug-likeness and absorption parameters. Molecular docking was performed to estimate the binding affinity of candidate ligands toward the mutant CHRNA5 protein, while Density Functional Theory (DFT) analysis was conducted to determine their electronic, reactive, and electrostatic properties for final lead selection. Results Functional prediction tools, including PolyPhen-2 and Alpha Missense, predict the mutation to be pathogenic with high scores predicting deleterious effects. Structural modeling with AlphaFold showed conformational changes in the mutant CHRNA5 protein, especially at the active site, validated with structural alignment. For therapeutic potential, 20 structurally similar aripiprazole ligands were identified through Swiss Similarity. Molecular docking indicated ligand 12 and ligand 4 had the highest binding affinity for mutant protein (-3.034 and -2.774 kcal/mol, respectively). PyMOL visualization indicated ligand-receptor interactions. ADMET analysis predicted good pharmacokinetics: Ligand 12 indicated high gastrointestinal absorption and CYP non-inhibition, while ligand 4 was a non-P-gp substrate with good solubility. DFT analysis indicated ligand 4 was the most polar, while ligand 12 was the most chemically reactive. Conclusion These findings imply the D398N mutation predisposes to addiction susceptibility and that ligand 12 holds promise as a therapeutic target due to its high binding affinity and good pharmacological profiles.

Low-cost biosurfactant production by Achromobacter xylosoxidans PX106473 from waste frying oil: partial characterization and antimicrobial mechanism via molecular docking.

Interest in microbial biosurfactants has increased due to the rising demand for environmentally friendly and sustainable surfactants. Waste frying oil provides a renewable and low-cost feedstock for their production. This study aimed to isolate, characterize, and evaluate the antibacterial mechanism of a biosurfactant synthesized by Achromobacter xylosoxidans PX106473 using waste frying oil as an economical carbon source. Achromobacter xylosoxidans PX106473 produced a biosurfactant with significant activity, including an emulsification index (E24%) of 66.7% against kerosene and substantial oil displacement and hemolytic activities. According to the results of thin-layer chromatography (TLC), the produced biosurfactant contained lipids and amino acids. Fourier transform infrared spectroscopy (FT-IR) results revealed the presence of an N-H group, aliphatic hydrocarbons, and amide peaks, which suggest a lipid-peptide linkage, providing further evidence for its putative lipopeptide nature. Hexadecanoic acid, with an area percentage of 76.44, was the dominant component of the lipopeptide based on gas chromatography-mass spectrometry (GC-MS) results. The produced biosurfactant demonstrated good inhibitory activity against E. coli and S. aureus. These biological findings were further supported by in silico assays; molecular docking studies showed that hexadecanoic acid binds stably to key bacterial proteins from E. coli (DNA gyrase B, -6.4 kcal/mol) and S. aureus (PBP2a, -3.9 kcal/mol), indicating a potential dual-target mechanism. Achromobacter xylosoxidans efficiently produced a putative lipopeptide biosurfactant from waste frying oil with strong emulsifying and antibacterial properties, providing an economical and sustainable solution with potential in various environmental and pharmaceutical applications.

Needle-in-a-haystack approach: rapid screening of PDE1C inhibitors through the combination of machine learning, molecular docking, molecular dynamics simulations and experimental validation.

Synthesis and antitumor property of triazole-aryloxyacetyl hydrazide hybrids with furo[2,3-d]pyrimidine scaffold.

This study describes the design and synthesis of a novel series of triazole-aryloxyacetyl hydrazide hybrids with furo[2,3-d]pyrimidine scaffold. The structure elucidations were performed using 1 H NMR, 13 C NMR and high-resolution mass spectrometry (HR-MS). The in vitro antitumor activities evaluation indicated that several derivatives exhibited considerable inhibiting effect against HepG2 cell lines, with IC50 values ranging from 4.82 to 69.83 Āµmol/L for compounds 5a-5l, 18.37 to > 100 Āµmol/L for compounds 6a-6j, 11.21 and 6.07 Āµmol/L for compounds 7a and 7b, respectively. Among them, compounds 5d and 5e appeared the most potent activity, along with notably lower cytotoxicity against the normal human liver cell line and the selectivity indices (SIs) were calculated to be 3.36 and 3.02, respectively, underscoring their antitumor potential. Furthermore, compound 5d not only induced concentration-dependent apoptosis but also effectively suppressed HepG2 cell migration. Following molecular docking studies demonstrated that compound 5d interacts with EGFR in a manner similar to gefitinib, suggesting its potential as an EGFR inhibitor. Preliminary comparative structure-activity relationship revealed that the bicyclic furo[2,3-d]pyrimidine core bearing a C-4 aryloxyacetyl hydrazide moiety was crucial for high activity, outperforming the tricyclic triazole-containing analogs (6a-6j). In addition, the introduction of halogen substituents (F, Cl) at the R1 position enhanced potency, as evidenced by compounds 5d, 5e, and 7b. Conversely, the tricyclic series (6a-6j) showed diminished activity. A comparison with the potent compounds 7a and 7b suggests that this may be due to unfavorable steric effects. This insight provides a direction for subsequent molecular optimization efforts.

Structure-based computational screening and molecular dynamics reveal potential inhibitors of Norovirus VP1 and RdRp Proteins: an in-silico study

Abstract Norovirus is recognized as a pathogen with pandemic potential, exhibiting a higher fatality rate in low-income countries, particularly affecting young children. Currently, vaccine or specific antiviral treatment for norovirus is lacking. For evaluating antiviral compounds, this study was conducted by targeting viral protein 1 (VP1) and RNA-dependent RNA polymerase (RdRP) proteins through in silico approach, which included modeling protein, assessment stability, molecular docking, molecular simulation, non-covalent interaction (NCI) analysis, and pharmacokinetic profiling. Higher binding affinity revealed by molecular docking (-7.8 kcal/mol to -9.4 kcal/mol) for the ligands Zingiberol, Cardeonolide, Boeravinone B, Beta-Elemene, and Fisetin with VP1 and RdRp. The molecular dynamic simulation and subsequent analyses demonstrated significantly expected stable docked complexes of the protein-ligand in comparison to the ribavirin antiviral drug. Furthermore, these ligands exhibited acceptable drug-likeness and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) profiles. This study initially suggests that these compounds have potentiality as antiviral candidates, warranting wet lab experiment to be considered for norovirus infection.

šŸ’” Pipeline Tip

Use Snakemake for reproducible end-to-end protein design workflows.

šŸ› ļø Resources

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

BS HF DK