Multi-target immunomodulatory actions of Elaeagnus rhamnoides L.) A. Nelson phytocompounds on immune regulatory targets via integrative network pharmacology, docking, and molecular simulation

Sepsis, marked by hyperinflammation and subsequent immunosuppression, lacks effective phase-specific therapies. Although anisodamine hydrobromide (Ani HBr) reduced 28-day mortality in our prior trial, its mechanisms remained unclear. Here, we integrated network pharmacology, machine learning, immunological profiling, molecular simulations, and single-cell transcriptomics to elucidate Ani HBr's multi-target actions. Among 30 cross-species targets, ELANE and CCL5 emerged as core regulators via protein interaction networks, survival modeling (AUC: 0.72-0.95), and statistical significance (p < 0.05). Ani HBr inhibited ELANE-driven NET formation (HR = 1.176), associated with immunosuppression and endothelial damage, while enhancing CCL5-related cytotoxic T-cell recruitment (HR = 0.810). Docking and dynamics simulations showed Ani HBr binds ELANE's catalytic cleft, suggesting direct inhibition of its enzymatic activity, and interacts stably with CCL5 at potential receptor-binding interfaces, indicating a modulatory role. Single-cell analysis revealed ELANE upregulation in CCI-phase neutrophils and widespread yet stage-specific CCL5 expression. These findings support Ani HBr as a phase-tailored agent that targets ELANE in early hyperinflammation while preserving CCL5-mediated immunity. The ELANE/CCL5 prognostic model offers a framework for precision immunotherapy in sepsis.

Network pharmacology integrated with multi-omics demonstrates that Wumei Wan modulates the progression of colorectal cancer by regulating the focal adhesion-YAP signaling axis.

Integrative network pharmacology, molecular docking, and dynamic simulation analysis of a polyherbal formulation for potential therapeutic impact on prostate cancer

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

BackgroundMolecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. The Python workflow utilizes molecular dynamics software (NAMD (Phillips et al., J Comput Chem 26(16):1781-802, 2005), VMD (Humphrey et al., J Mol Graph 14(1):33-8, 27-8, 1996)) to generate variant specific scaffolds for simulated small molecule docking (AutoDock Vina (Trott and Olson, J Comput Chem 31(2):455-61, 2010)).ResultsSNP2SIM is composed of three independent modules that can be used sequentially to generate the variant scaffolds of missense protein variants from the wildtype protein structure. The workflow first generates the mutant structure and configuration files required to execute molecular dynamics simulations of solvated protein variant structures. The resulting trajectories are clustered based on the structural diversity of residues involved in ligand binding to produce one or more variant scaffolds of the protein structure. Finally, these unique structural conformations are bound to small molecule ligand libraries to predict variant induced changes to drug binding relative to the wildtype protein structure.ConclusionsSNP2SIM provides a platform to apply molecular simulation based functional analysis of sequence variation in the protein targets of small molecule therapies. In addition to simplifying the simulation of variant specific drug interactions, the workflow enables large scale computational mutagenesis by controlling the parameterization of molecular simulations across multiple users or distributed computing infrastructures. This enables the parallelization of the computationally intensive molecular simulations to be aggregated for downstream functional analysis, and facilitates comparing various simulation options, such as the specific residues used to define structural variant clusters. The Python scripts that implement the SNP2SIM workflow are available (SNP2SIM Repository. https://github.com/mccoymd/SNP2SIM, Accessed 2019 February ), and individual SNP2SIM modules are available as apps on the Seven Bridges Cancer Genomics Cloud (Lau et al., Cancer Res 77(21):e3-e6, 2017; Cancer Genomics Cloud [www.cancergenomicscloud.org; Accessed 2018 November]).

BackgroundFactor C (FC) is widely used as a standard material for endotoxin testing. It functions as a zymogenic serine protease and serve as a biosensor that detects lipopolysaccharides. Prior investigations involving molecular docking and molecular dynamics simulations of FC demonstrated an interaction between the C-type lectin domain (CLECT) and the ligand lipopolysaccharide (lipid A). In this study, our aim was to assess the stability of the interaction between fragment FC and the lipid A ligand using protein modeling approaches, molecular docking, molecular dynamics simulation, and gene construction into the pPIC9K expression vector. Methods and resultsThe FC structure was modelled by online tools. In this case, both molecular docking and MD simulations were applied to identify the interaction between protein and ligand (lipid A) including its complex stability. The FC structure model using three modeling websites has varied values, according to a Ramachandran plot study. When compared to other models, AlphaFold server modeling produced the best Ramachandran findings, with residues in the most advantageous area at 88.3%, followed by ERRAT values at 89.83% and 3D Verify at 71.93%. From the docking simulation of FC fragments with three ligands including diphosphoryl lipid A, FC-Core lipid A, and Kdo2 lipid A can be an activator of FC protein by binding to receptor regions to form ligand-receptor complexes. MD simulations were performed on all three complexes to assess their stability in water solvents showing that all complexes were stable during the simulation. The optimization of recombinant protein expression in Pichia pastoris was conducted by assessing the OD value and protease activity. Induction was carried out using 1% (v/v) methanol in BMMY media at 30°C for 72 h. ConclusionsProtein fragments of Factor C has been proven to detect endotoxins and serve as a potential biomarker. Molecular docking simulation and MD simulation were employed to study the complex formation of protein fragments FC with ligands. The expression of FC fragments was successfully achieved through heterologous expression. We propose optimizing the expression of FC fragments by inducing them with 1% methanol at 30°C and incubating them for 72 h. These optimized conditions are well-suited for upscaling the production of recombinant FC fragments using a bioreactor.

Biomarker identification and inhibitor discovery for Marburg virus using machine learning driven virtual screening and molecular simulations

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma

Luteolin and chrysoeriol, active flavonoids found in Fagopyrum dibotryis Rhizoma (FDR), possess similar molecular structures and demonstrate anti-tumor activity; however, their efficacy and mechanisms in non-small cell lung cancer (NSCLC) are still incomplete. This research aimed to reveal the therapeutic potential and mechanisms of these phytoconstituents in NSCLC through an integrated strategy of network pharmacology, molecular docking, and experimental validation. We used network pharmacology to discover possible targets and pathways for luteolin and chrysoeriol in NSCLC, which involved predicting targets, constructing protein-protein interaction (PPI) networks, and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and dynamics simulations assessed the binding affinities of both phytoconstituents to core targets. Hub gene expression in NSCLC tissues was further examined using bioinformatics tools. The anti-NSCLC effects were evaluated by measuring A549 and PC9 cell viability, migration, apoptosis, and modulation of the PI3K/AKT pathway. We identified 64 potential therapeutic targets for NSCLC. Enrichment analysis revealed the PI3K-Akt signaling pathway as the most significantly associated. Molecular simulations indicated stable binding of both phytoconstituents to core targets, with luteolin exhibiting stronger binding affinity. In experimental validation, luteolin more potently inhibited NSCLC cell viability and migration, alleviated mitochondrial damage, and induced apoptosis relative to chrysoeriol. Luteolin also more effectively regulated PI3K/AKT signaling. Both luteolin and chrysoeriol represent promising natural agents for NSCLC treatment, with luteolin demonstrating superior bioactivity.

Knee osteoarthritis (KOA) is a chronic degenerative joint disorder driven largely by persistent inflammation and progressive cartilage damage. Naringin, a bioactive flavonoid abundant in citrus fruits, has shown potential anti-inflammatory effects; however, its molecular mechanisms in KOA remain unclear. In this study, an integrated approach combining network pharmacology, molecular docking, molecular dynamics (MD) simulations, and in vitro experiments was employed to investigate the anti-inflammatory effects of naringin in KOA. Network pharmacology analysis identified 59 potential KOA-related targets of naringin, among which TNF, PTGS2, TP53, CASP3, and PPARG were recognized as core targets. Functional enrichment indicated these targets were primarily associated with inflammation- and apoptosis-related pathways, especially the TNF and IL-17 signaling pathways. Molecular docking and MD simulations revealed strong binding affinity and stable interactions between naringin and the key inflammatory mediators TNF-α and PTGS2. In an IL-1β-stimulated C28/I2 human chondrocyte model, naringin dose-dependently improved cell viability and significantly suppressed TNF-α and PTGS2 expression at both mRNA and protein levels. These findings provide mechanistic evidence that naringin alleviates KOA-associated chondrocyte inflammation by modulating key inflammatory mediators, supporting its potential as an anti-inflammatory therapeutic candidate for KOA.

Vascular dementia (VaD), a cognitive impairment resulting from cerebrovascular issues, could be mitigated by Epimedium. This study investigates Epimedium's efficacy in VaD management through a systematic review, network pharmacology, molecular docking, and molecular dynamic simulations (MDS). Comprehensive literature searches were conducted across various databases. Epimedium's pharmacological properties were analyzed using the TCMSP database. Integration with the Aging Atlas database enabled the identification of shared targets between Epimedium and VaD. A protein-protein interaction (PPI) network was constructed, and central targets' topological attributes were analyzed using Cytoscape 3.9.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using “ClusterProfiler” R package. The interactions between Epimedium and central targets were assessed by Molecular docking and MDS. Epimedium and its 23 bioactive components counteracted oxidative stress, neuroinflammation, and neuronal damage, thereby attenuating cognitive deterioration in VaD. A total of 78 common targets were identified, with 22 being significantly related to aging. Enrichment analysis identified 1769 GO terms and 139 KEGG pathways, highlighting the AGE-RAGE signaling pathway. Molecular docking revealed that 23 bioactive components, except Linoleyl acetate, effectively interacted with top central targets (JUN, MAPK14, IL6, FOS, TNF). MDS demonstrated that flavonoids Icariin, Kaempferol, Luteolin, and Quercetin formed stable complexes with RAGE. The study identifies RAGE as a novel therapeutic target for Epimedium in the mitigation of VaD via its anti-inflammatory properties.

Oleanolic acid (OA) has demonstrated anticancer effects across various cancers, with some derivatives advancing to clinical trials. Howe ver, its precise mechanisms of action remain unclear, especially in oral squamous cell carcinoma (OSCC). This study employed network pharmacology, bioinformatics, molecular docking, dynamics simulations, and experimental validation to explore OA's anticancer effects in OSCC and elucidate its mechanism of action. OA's pharmacokinetic and physicochemical properties were assessed using SwissADME and Molsoft, revealing high oral bioavailability and GI absorption. SwissTargetPrediction and SuperPred identified protein targets, whereas GeneCards provided OSCC-related targets. A Venn diagram showed 34 overlapping targets between OA and OSCC. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network with 32 nodes and 164 edges, identifying HSP90AA1, STAT3, HSP90AB1, PI3KR1, and NFKB1 as key hub genes. Gene ontology and KEGG enrichment analyses highlighted relevant biological processes, molecular functions, and pathways. Molecular docking and dynamics simulations confirmed the strong binding of OA to hub targets. Experimental validation showed that OA inhibited cell viability and colony formation in a dose-dependent manner, induced apoptosis, and downregulated HSP90AA1, STAT3, and PI3KR1 proteins. In conclusion, this comprehensive study combining network pharmacology, bioinformatics, molecular simulations, and experimental assays provides valuable insights into OA's anticancer potential and detailed mechanism of action in OSCC.

By examining the interactions between the protein hen egg-white lysozyme (HEWL) and commercially available and chemically synthesized carbohydrate ligands using a combination of weak affinity chromatography (WAC), NMR spectroscopy and molecular simulations, we report on new affinity data as well as a detailed binding model for the HEWL protein. The equilibrium dissociation constants of the ligands were obtained by WAC but also by NMR spectroscopy, which agreed well. The structures of two HEWL-disaccharide complexes in solution were deduced by NMR spectroscopy using (1)H saturation transfer difference (STD) effects and transferred (1)H,(1)H-NOESY experiments, relaxation-matrix calculations, molecular docking and molecular dynamics simulations. In solution the two disaccharides β-d-Galp-(1→4)-β-D-GlcpNAc-OMe and β-D-GlcpNAc-(1→4)-β-D-GlcpNAc-OMe bind to the B and C sites of HEWL in a syn-conformation at the glycosidic linkage between the two sugar residues. Intermolecular hydrogen bonding and CH/π-interactions form the basis of the protein-ligand complexes in a way characteristic of carbohydrate-protein interactions. Molecular dynamics simulations with explicit water molecules of both the apo-form of the protein and a ligand-protein complex showed structural change compared to a crystal structure of the protein. The flexibility of HEWL as indicated by a residue-based root-mean-square deviation analysis indicated similarities overall, with some residue specific differences, inter alia, for Arg61 that is situated prior to a flexible loop. The Arg61 flexibility was notably larger in the ligand-complexed form of HEWL. N,N'-Diacetylchitobiose has previously been observed to bind to HEWL at the B and C sites in water solution based on (1)H NMR chemical shift changes in the protein whereas the disaccharide binds at either the B and C sites or the C and D sites in different crystal complexes. The present study thus highlights that protein-ligand complexes may vary notably between the solution and solid states, underscoring the importance of targeting the pertinent binding site(s) for inhibition of protein activity and the advantages of combining different techniques in a screening process.

Decoding the air pollutant-psoriasis axis: A multi-layered systems toxicology investigation.

CD44 is a cell-surface glycoprotein and receptor for hyaluronan, one of the major components of the tumor extracellular matrix. There is evidence that the interaction between CD44 and hyaluronan promotes breast cancer metastasis. Recently, the molecule F-19848A was shown to inhibit hyaluronan binding to receptor CD44 in a cell-based assay. In this study, we investigated the mechanism and energetics of F-19848A binding to CD44 using molecular simulation. Using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) method, we obtained the binding free energy and inhibition constant of the complex. The van der Waals (vdW) interaction and the extended portion of F-19848A play key roles in the binding affinity. We screened natural products from a traditional Chinese medicine database to search for CD44 inhibitors. From combining pharmaceutical requirements with docking and molecular dynamics simulations, we found ten compounds that are potentially better or equal to the F-19848A ligand at binding to CD44 receptor. Therefore, we have identified new candidates of CD44 inhibitors, based on molecular simulation, which may be effective small molecules for the therapy of breast cancer.