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.

Signal of the Day

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.

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

Also Worth Reading

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.

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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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

Pipeline Tip

Index your BigWig files before visualization to save memory.

Resources & Tools

• 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