Issue #11: 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

AlphaFold for Docking Screens.

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Modeling Protein-Protein Complexes by Combining pyDock and AlphaFold.

The lack of experimental structures for the majority of protein-protein complexes has motivated the development of a variety of strategies for the structural modeling of protein complexes, such as computational docking, in active development for the last decades, and the more recent artificial intelligence (AI)-based ground-breaking methodologies. Among the existing computational docking methods, Python docking (pyDock) has shown competitive predictive rates and high robustness over the years. However, the field has dramatically changed with the appearance of artificial intelligence (AI)-based methods, like AlphaFold. While structure prediction of individual proteins is virtually solved by this program, the focus is now on how to improve the prediction of challenging cases like antibody-antigen complexes, multiprotein complexes, weak interactions, or highly flexible interacting proteins. Successful strategies are based on the generation of more diverse sets of models and the integration with other “classical” approaches that facilitate the identification of the correct models. Here, we will show in practical terms how to combine the structural modeling capabilities of AlphaFold with the energy-based scoring function in pyDock to improve structural predictions in challenging protein-protein complexes.

Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability.

Proteins perform diverse functions critical to cellular processes. Transitions between functional states are often regulated by post-translational modifications (PTMs) such as phosphorylation, which dynamically influence protein structure, function, folding, and interactions. Dysregulation of PTMs can therefore contribute to diseases such as cancer and Alzheimer’s. However, the structure-function relationship between proteins and their modifications remains poorly understood due to a lack of experimental structural data, the inherent diversity of PTMs, and the dynamic nature of proteins. Recent advances in deep learning, particularly AlphaFold, have transformed protein structure prediction with near-experimental accuracy. However, it remains unclear whether these models can effectively capture PTM-driven conformational changes, such as those induced by phosphorylation. Here, we systematically evaluated AlphaFold models (AF2, AF3-non phospho, and AF3-phospho) to assess their ability to predict phosphorylation-induced structural diversity. By analyzing experimentally derived conformational ensembles, we found that all models predominantly aligned with dominant structural states, often failing to capture phosphorylation-specific conformations. Despite its phosphorylation-aware design, AF3-phospho predictions provided only modest improvement over AF2 and AF3-non phospho predictions. Our findings highlight key challenges in modeling PTM-driven structural landscapes and underscore the need for more adaptable structure prediction frameworks capable of capturing modification-induced conformational variability.

🧪 AI & Research News

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🏢 Industry Insight & Applications

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⚡ Quick Reads

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 mice 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.

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.

Assessing the validity of leucine zipper constructs predicted by AlphaFold.

AP-1 transcription factors are a network of cellular regulators that combine in different dimer pairs to control a range of pathways involved in differentiation, growth, and cell death. They dimerize via leucine zipper coiled-coil domains that are preceded by a basic DNA binding domain. Depending on which AP-1 transcription factors dimerize, different DNA sequences will be recognized resulting in differential gene expression. The affinity of AP-1 transcription factors for each other dictates which dimers form. The relative concentration of AP-1 transcription factors varies with tissue type and environment, adding another layer of control to this integral network of cellular regulation. The development of artificial intelligence (AI)-based protein structure prediction methods gives us a new technique to investigate or predict how dimerization affects combinatorial control. All versions of AlphaFold2 and AlphaFold3 are AI/deep learning programs that predict 3D structures of proteins from an amino acid sequence and multiple sequence alignments of homologous proteins. To fully realize the potential of AI for structural biology, it is essential to understand its current capabilities and limitations. In this study, we used the classical example of an AP-1 dimer: Fos and Jun, and an array of over 2000 experimentally tested human leucine zippers to interrogate how AlphaFold models leucine zipper domains and if AlphaFold can be used to differentiate between probable and improbable dimer interfaces. We found that AlphaFold predicts highly confident leucine zipper dimers, even for dimer pairs such as the FosB homodimer, for which electrostatics are known to prevent their formation in vivo. This is an important case study concerning high-confidence but low-accuracy protein structure prediction.

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.

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.

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.

Stable de novo protein design via joint conformational landscape and sequence optimization.

Generative protein modeling provides advanced tools for designing diverse protein sequences and structures. However, accurately modeling the conformational landscape and designing sequences remain critical challenges: ensuring that the designed sequence reliably folds into the target structure as its most stable conformation, and optimizing the sequence for a given suboptimal fixed input structure. In this study, we present a systematic analysis of jointly optimizing sequence-to-structure and structure-to-sequence mappings. This approach enables us to find optimal solutions for modeling the conformational landscape. We validate our approach with large-scale protein stability measurements, demonstrating that joint optimization is superior for designing stable proteins using a joint model (TrRosetta and TrMRF) and for achieving high accuracy in stability prediction when jointly modeling (half-masked ESMFold pLDDT + ESM2 Pseudo-likelihood). We further investigate features of sequences generated from the joint model and find that they exhibit higher frequencies of hydrophilic interactions, which may help maintain both secondary structure registry and pairing-features not captured by structure-to-sequence modeling alone.

💡 Pipeline Tip

Employ HADDOCK for ambiguous restraints in protein-protein docking.

🛠️ Resources

• Dataset: PDB-REDO - Optimized protein structure database with refined models.
• Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server. View all tools →
• Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging. View all tools →
• Event: Structural Biology Events (Open)
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Job: Prima Mente hiring GPU Accelerated Bioinformatics Engineer in San Francisco, CA - LinkedIn at Bioinformatics Careers
• Job: Mercor hiring Computational Bioinformatics Analyst in United Kingdom - LinkedIn at Bioinformatics Careers

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