Issue #9: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🚀 Today’s Top Signal

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

⭐ Additional Signals

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Zhenwu decoction (ZWD), a traditional Chinese medicine, has shown therapeutic potential in alleviating MDD symptoms. However, its complex composition has limited the understanding of its underlying pharmacological mechanisms. This study aimed to explore the antidepressant mechanisms of ZWD in the treatment of MDD. Methods Active compounds and potential targets of ZWD were identified through database screening and network pharmacology analysis. These targets were intersected with MDD-related genes to construct a protein-protein interaction network. Core targets were further refined using random forest algorithms. Molecular docking and molecular dynamics simulations were employed to evaluate the binding affinity and stability between key compounds and core targets. Experimental validation was conducted in a lipopolysaccharide (LPS)-induced mouse model of depression using behavioral testing, measurement of inflammatory cytokines, and gene expression analysis. Results Network pharmacology and machine learning identified TNF-α signaling as key pathways in the antidepressant effects of ZWD. Enrichment analysis highlighted the involvement of Lipid and atherosclerosis, the IL-17 signaling pathway. Core targets, including PPARG, F10, AR, TNF, PIK3CG, ADH1C, and GABRA6, were predicted to mediate its effects. Molecular docking and dynamics simulations confirmed strong binding of ZWD components, especially kaempferol, to TNF-α, inhibiting its expression. In vivo, ZWD improved anxiety/depressive-like behaviors in LPS-treated mice, evidenced by better performance in the behavioral tests. ZWD also reduced neuroinflammation, with decreased Tnf-α levels, and reduced IBA-1 and GFAP staining, indicating reduced microglial and astrocyte activation. These results suggest that ZWD alleviates depression through modulation of TNF-α-mediated inflammation. Conclusions These findings suggest that ZWD exerts antidepressant effects primarily by modulating TNF-α-mediated inflammatory pathways, providing a comprehensive molecular and experimental framework supporting its clinical potential in MDD treatment.

A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics.

Molecular dynamics (MD) simulation is a key tool in drug discovery for predicting protein-ligand binding affinities, transport properties, and pocket dynamics. While advances in numerical and machine learning (ML) methods have improved MD efficiency, accurately modeling long-timescale dynamics remains challenging. We introduce NeuralMD, an ML surrogate that accelerates and enhances MD simulations of protein-ligand binding. NeuralMD employs a physics-informed, multi-grained, group-symmetric framework comprising (1) BindingNet, which enforces symmetry via vector frames and captures multi-level protein-ligand interactions, and (2) an augmented neural differential equation solver that learns trajectories under Newtonian mechanics. Across ten single-trajectory and three multi-trajectory tasks, NeuralMD achieves up to 15 × lower reconstruction error and 70% higher validity than existing ML baselines. The predicted oscillations closely align with ground-truth dynamics, establishing NeuralMD as a foundation for next-generation protein-ligand simulation research.

Exploring the Mechanism of Platycladi Cacumen in Intervening Androgenetic Alopecia Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Abstract As a traditional hair-growth-promoting herb, Platycladi Cacumen(PC) has a long history of folk application in the field of hair loss improvement. Preliminary modern pharmacological studies have suggested that its active components may exert potential effects by regulating hair follicle-related signaling pathways; however, for androgenetic alopecia (AGA), the exact targets and specific regulatory mechanisms of PC remain unelucidated, which provides a direction for research on natural drug-based intervention in AGA. In this study, network pharmacology was employed to predict the active components and core targets of PC. Targets associated with AGA were collected, and the intersection targets between PC and AGA were identified. Subsequently, protein-protein interaction (PPI) analysis, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the intersection targets to screen out the core targets. Thereafter, molecular docking and molecular dynamics simulation were conducted to validate the interactions between key active components and core targets. The component-target network diagram included 1044 interaction relationships between 32 components and 439 targets, among which quercetin, apigenin, myricetin, and hinokinin were identified as key components. The disease-target network diagram summarized 410 targets associated with AGA. Through PPI network analysis, key targets such as ESR1, BCL2, INS, AR, and STAT3 were screened out. The results of GO enrichment analysis and KEGG pathway analysis revealed that PC may exert its effects by regulating the EGFR receptor molecule and pathways including the HIF-1 signaling pathway. Molecular docking results showed that the binding energies of all complexes were less than -6.4 kcal/mol, indicating favorable binding effects. Molecular dynamics simulation results showed that the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), two-dimensional free energy landscape (FEL-2D), and FEL-3D of the simulation system all remained in an equilibrium state with small fluctuation amplitudes. This result indicated that the molecular system had a stable overall conformation, restricted local residue movement, a compact spatial structure, and stable internal chemical bonds—collectively confirming that the quercetin-STAT3, apigenin-AR, myricetin-STAT3, and hinokinin-AR complexes exhibited extremely strong binding stability. Collectively, Overall, this study systematically investigated the mechanism of action and potential value of PC leaves in intervening in AGA, providing a solid theoretical basis for the intervention of AGA with PC.

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⚡ Quick Reads

Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.

The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Curcumin, a natural compound, has shown potential in alleviating angina and improving metabolic risk factors in CSFP, but its underlying molecular mechanisms remain unclear. This study employed an integrated computational strategy. Network pharmacology was used to identify potential targets of curcumin and CSFP from public databases, and common targets were identified. Functional enrichment analysis was performed on the common targets, and a protein-protein interaction network was constructed. Core targets were identified using MCODE and CytoHubba plugins in Cytoscape. Molecular docking evaluated the binding modes and affinities of curcumin with the core targets, while molecular dynamics simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations validated the stability and binding free energies of the complexes. A total of 120 predicted targets of curcumin and 435 CSFP-related targets were identified, yielding 19 common targets. Functional enrichment analysis revealed that curcumin may treat CSFP by modulating inflammatory response, vascular function, cell migration, proliferation, apoptosis, and oxidative stress. These targets were associated with key signaling pathways, including NF-κB, TNF, and HIF-1. Network analysis and topological algorithms identified five core targets: EGFR, ICAM1, NFKB1, PTGS2, and STAT3. Molecular docking results demonstrated that curcumin exhibited excellent binding affinity with all core targets. Molecular dynamics simulations confirmed that the curcumin-core target complexes remained structurally stable during the 100 ns simulation, and MM/GBSA calculations indicated significantly negative binding free energies, suggesting strong binding driving forces. Curcumin may exert therapeutic effects on CSFP through a multi-target mechanism, primarily by interacting with key proteins including EGFR, ICAM1, NFKB1, PTGS2, and STAT3, thereby regulating the NF-κB, TNF, and HIF-1 signaling pathways. This study provides a theoretical foundation for the application of curcumin in CSFP treatment, though further experimental validation is required.

A screening strategy for bioactive components from Amaranth: An integrated approach of network pharmacology, molecular docking and molecular dynamics simulation.

Amaranth is a traditional medicinal and forage plant with promising anti-inflammatory properties. To enhance its utilization in livestock and feed industries, this study investigated the bioactive compounds and mechanisms of Amaranth at different growth stages using metabolomics and network pharmacology. LC-MS/MS identified 266 metabolites, including key compounds such as ferulic acid, isoferulic acid, sinapic acid, and 13-HODE. A total of 132 inflammation-related targets were screened, and enrichment analysis revealed their involvement in ATP binding, inflammatory response, and PI3K-Akt/MAPK signaling pathways. Molecular docking and molecular dynamics simulations confirmed strong interactions between core targets (e.g., IL6, MMP9) and major compounds. These findings demonstrate that phenolic acids and fatty acids in Amaranth possess anti-inflammatory activity, underpinning its prospective use in the formulation of biofunctional feeds and in promoting the health of livestock.

Molecular docking and molecular dynamics study of PUFAs from <i>Navicula salinicola</i>: prospective antiviral strategies targeting the SARS-CoV-2 spike protein.

The emergence of novel viral infections, such as SARS-CoV-2, H5N2, and H7N9, among recently identified viruses, has highlighted the urgent need for new antiviral therapeutics. The SARS-CoV-2 virus binds to ACE2 on host cell surfaces, reducing ACE2 expression, increasing Angiotensin II, and activating the RAAS system. On the other sides, marine organisms like Navicula salinicola are a significant unexplored source of bioactive compounds with potential antiviral activity. However, investigation on marine-derived poly-unsaturated fatty acids (PUFAs) as antiviral agents for SARS-CoV-2 is a priority, as they have shown promising antiviral properties. This exploration highlights the ongoing efforts to explore natural compounds for their therapeutic potential against viral infections, including COVID-19. This study aims to investigate the antiviral potential of PUFAs from N. salinicola against SARS-CoV-2 using molecular docking and molecular dynamics simulations. A total of 14 PUFAs from N. salinicola were subjected to molecular docking with the SARS-CoV-2 spike protein, and the three best-ranked ligands, DHA (- 7.56 kcal/mol), AA (- 6.61 kcal/mol), and EPA (- 6.5 kcal/mol), were further analyzed by molecular dynamics. Our study identified all PUFAs with promising binding affinities toward the SARS-CoV-2 spike protein, suggesting their potential as effective inhibitors of viral entry or replication mechanisms. Molecular dynamics simulations revealed that the ligands docosahexaenoic acid (DHA), arachidonic acid (AA), and eicosapentaenoic acid (EPA) exhibited ∆ETotal values of - 46.45, - 48.31, and - 43.65 kcal/mol, respectively, indicating a relatively stable interaction with ACE2. AA, the most potent marine-derived PUFA from N. salinicola , has been identified as the lead compound for SARS-CoV-2 inhibitors, requiring further research for in vitro or in vivo experiments.

Genome-wide analysis, expression profiling and molecular docking of tomato (Solanum lycopersicum) calmodulin (SlCaM) proteins in cadmium stress adaptation.

Calcium ions (Ca 2+ ) are essential for plant development and stress responses, including heavy metal (HM) stress. However, the roles and mechanisms of calmodulin proteins (SlCalMs) in mediating cadmium (Cd) stress in Solanum lycopersicum, a model crop, remain poorly understood. This study aimed to investigate the calcium-mediated stress response in S. lycopersicum by identifying and characterizing the SlCalMs gene family, a key subfamily of calcium-binding proteins (CBPs), to elucidate their potential roles in stress tolerance. A genome-wide identification of SlCalMs was conducted using Oryza sativa sequences as a reference. Bioinformatics analyses included BLASTP searches, sequence alignment, phylogenetics, assessment of physicochemical properties, gene structure and motif analysis, chromosomal mapping and duplication events. Gene expression was assessed under Cd stress using RNA-seq and validated by quantitative real-time polymerase chain reaction (qRT-PCR). Molecular docking simulations evaluated Cd-binding affinities, and protein-protein interaction networks, and Gene Ontology (GO) enrichment were used to explore biological functions. Eight distinct SlCalM groups were identified, varying in gene size, exon number and isoelectric point. Conserved motifs, exon-intron patterns and stress-responsive cis-elements were identified. Chromosomal analysis revealed segmental duplications. Under Cd stress, several SlCalMs showed differential expression; notably, Solyc04g077830 was significantly downregulated and showed strong Cd-binding affinity in silico, suggesting a role in Cd sequestration. GO and interaction network analyses confirmed their involvement in Ca 2+ signalling, metal ion binding and stress-related pathways. This study provides comprehensive insight into the structure, evolution and functional roles of SlCalMs in tomato. Their involvement in Ca 2+ signalling and Cd stress response highlights their potential for improving HM tolerance, offering valuable targets for future genetic or biotechnological interventions in crop improvement.

The Mechanism of <i>Andrographis paniculata</i> in the Treatment of Influenza Explored via Network Pharmacology and Molecular Docking.

Objective The objective of this study is to investigate the potential mechanisms of Andrographis paniculata in treating influenza using network pharmacology and molecular docking approaches. Methods The active components of A. paniculata were identified through the traditional Chinese medicine systems pharmacology database (TCMSP), and potential targets were predicted using SwissTargetPrediction. Gene targets associated with influenza were obtained from the GeneCards and OMIM databases. Venny 2.1.0 was used to create a Venn diagram to determine overlapping targets between A. paniculata and influenza. A “drug-component-target” interaction network was constructed using Cytoscape 3.7.2. A protein-protein interaction (PPI) network was developed with STRING 12.0 and visualized using Cytoscape 3.9.1 to identify core genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted via the DAVID database, and the results were visualized using an online bioinformatics platform. Molecular docking was performed between major components and core targets using AutoDock 4.2.6 software. Results A total of 24 active components of A. paniculata were identified, yielding 646 predicted drug targets, 1876 influenza-associated gene targets, and 176 intersecting targets. GO enrichment analysis revealed 919 terms, primarily related to inflammatory responses and protein phosphorylation. KEGG analysis identified 173 enriched pathways, notably those related to lipid metabolism, atherosclerosis, and cancer. The principal active compounds demonstrated strong binding affinities with the core targets. Conclusion A. paniculata may exert therapeutic effects against influenza by acting on core targets, such as TNF, IL-6, AKT1, GAPDH, and STAT3. These findings provide a scientific foundation for the application of traditional Chinese medicine in the treatment of influenza.

Investigating the therapeutic potential of pinocembrin in Alzheimer’s disease: insights from network pharmacology and molecular docking.

The complicated neurodegenerative disease known as Alzheimer’s disease (AD) is typified by neural malfunction, cognitive impairment, and gradual memory loss. Multi-target treatment approaches are desperately needed since AD etiology is complicated. Using network pharmacology, molecular docking, and in vitro experimental validation, this study explores the therapeutic potential of pinocembrin, a flavonoid recognized for its neuroprotective, antioxidant, and anti-inflammatory qualities. Network pharmacology study revealed nine important AD-associated targets of Pinocembrin, which are involved in neurotransmitter modulation, oxidative stress response, and neuronal protection. These targets include CA2, CYP1B1, CYP19A1, DPP4, ESR1, ESR2, HSP90AB1, MAOB, and SHBG. The relationship of these targets with important networks linked to AD, including PI3K-Akt signaling, estrogen signaling pathways, and neuroactive ligand-receptor interaction, was further validated by Gene Ontology (GO) and KEGG pathway enrichment analysis. According to ADMET study, Pinocembrin has good pharmacokinetic characteristics, such as low anticipated toxicity, intermediate blood-brain barrier permeability, and high gastrointestinal absorption. Strong and consistent binding affinities were shown by molecular docking studies, especially with CYP1B1 (-8.1 kcal/mol), DPP4 (-7.3 kcal/mol), and CA2 (-7.6 kcal/mol), indicating possible inhibitory effects on these targets. The compound’s medicinal property was further supported by in vitro validation. Pinocembrin’s safety profile was validated by the MTT assay, which demonstrated high cell survival (>90%) in PC12 neuronal cells at all tested dosages. In comparison to donepezil as a reference, pinocembrin also demonstrated moderate acetylcholinesterase (AChE) inhibitory action, with an IC₅₀ of 50 µM. Furthermore, DPPH, ABTS, and H 2 O 2 scavenging assays were used to indicate antioxidant activity. The IC₅₀ values for these assays were 150 µg/mL, 78.6 µg/mL, respectively, and total reducing power was 46.5 mg EAA/g. All of these results point to the possibility of pinocembrin as a multi-target therapy drug for Alzheimer’s disease. To verify its effectiveness and refine its pharmacological profile for therapeutic use, more in vivo research and molecular dynamics simulations are necessary.

Oxindole based sulfonyl derivatives synthesized as potent inhibitors of alpha amylase and alpha glucosidase along with their molecular docking study.

Diabetes mellitus, a persistent metabolic disorder, impedes the proper metabolism of proteins, carbohydrates, and lipids, leading to various physiological complications. A spectrum of synthetic alpha-glucosidase inhibitors is employed to mitigate glucose levels; however, prolonged use of these medications has been associated with a range of adverse effects. The current study particularly focuses on piperidin-indolin based sulfonyl derivatives, a class of heterocyclic compounds to assess the inhibitory efficacy of these synthesized compounds against α-amylase and α-glucosidase enzymes. All compounds showed excellent inhibitory activity in the range between 1.90 ± 0.10 to 16.80 ± 0.30 µM (amylase) and 1.20 ± 0.01 to 15.40 ± 0.30 µM (glucosidase). Limited structural activity relationship has been established for all compounds which suggest compound 16 has many folds better potential then standard drug. Molecular docking revealed that the most active compounds established stable hydrogen-bonding and hydrophobic interactions within the catalytic pockets of α-amylase and α-glucosidase, consistent with key active-site residues known to mediate inhibition. Molecular dynamics simulations further confirmed the stability of the ligand-enzyme complexes, particularly the α-glucosidase-compound 7 system, which maintained a Cα RMSD range of 1.5-2.2 Å throughout 200 ns. Binding free energy calculations using MM-GBSA yielded an average ΔG bind of approximately - 25 kcal mol⁻¹, with van der Waals and lipophilic forces providing the primary stabilizing contributions and electrostatic and solvation effects offering additional support.

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 ≥ 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.

💡 Pipeline Tip

Index your BigWig files before visualization to save memory.

🛠️ Resources

• Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
• Tool: ChimeraX - Next-gen molecular visualization for large data sets. View all tools →
• Tool: ReFOLD4 - Sophisticated protein structure refinement tool for improving model quality. View all tools →
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Event: Structural Biology Events (Open)
• Job: Mercor hiring Computational Bioinformatics Specialist in Greater Montreal Metropolitan Area - LinkedIn at Bioinformatics Careers
• Job: Prima Mente hiring GPU Accelerated Bioinformatics Engineer in London, England, United Kingdom - LinkedIn at Bioinformatics Careers

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