Combining Cryo-EM with Computational Approaches To Revolutionize Structural Biology.

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

To explore the molecular mechanism of Sijunzi Decoction (SJZD) in the treatment of Parkinson's disease (PD) through the application of network pharmacology, molecular docking, and molecular dynamics simulations, complemented by experimental verification. The BATMAN-TCM, GeneCards, and DisGeNet databases were searched to screen the active components and therapeutic targets of SJZD. Cytoscape (3.7.1) was used to create a network diagram of the components and targets. The STRING platform was used to construct a protein-protein interaction (PPI) network. The Bioconductor database and RX64 (4.0.0) software were used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the core target genes. The binding sites and binding energies between SJZD active components and the target were analyzed by molecular docking and dynamic simulation. Finally, the therapeutic effect and mechanism of SJZD were verified by Cell Counting Kit-8 (CCK-8) and Western blotting (WB). This research identified 188 active compounds in SJZD, 1568 drug targets, 2069 PD targets, and 451 intersection targets related to PD. According to network analysis, Adenosine Triphosphate, Tridecanoic Acid, Hexadecanoic Acid, Pentadecanoic Acid, and Adenosine were identified as the core components of SJZD in the treatment of PD. The five targets with the highest Degree values in the PPI network were AKT1, INS, TNF, IL-6, and TP53. The GO and KEGG enrichment analyses, in turn, determined that the administration of SJZD for the treatment of PD may engage processes such as xenobiotic stimulation and biological stimulus response. Furthermore, AGE-RAGE and cAMP signaling pathways related to diabetic complications may be involved. Molecular docking and kinetic simulations showed that IL-6 and AKT1 bind best to Adenosine. Experimental results showed that SJZD significantly reduced 6-OHDA-induced apoptosis of SH⁃SY5Y cells by activating the PI3K/AKT signaling pathway and regulating the expression of apoptosis factors such as Bcl⁃2 and Bax. SJZD is essential in the processes of apoptosis and neuronal protection, acting through various components that target multiple pathways. Notably, the PI3K/AKT pathway is a verified SJZD-PD target, providing a reference for clinical precision drug use for PD.

Fufang Honghua Buji (FHB) granules, have proven efficacy against vitiligo in long-term clinical practice. However, its major active chemical components and molecular mechanisms of action remain unknown. The purpose of this study was to confirm the molecular mechanism of FHB's therapeutic effect on vitiligo utilizing network pharmacology, molecular docking, and molecular dynamics simulation prediction, as well as experimental verification. Traditional Chinese Medicine Systems Pharmacology (TCMSP) and HERB databases were used to obtain the chemical composition and action targets of FHB. Online Mendelian Inheritance in Man (OMIM), DrugBank, DisGeNET, GeneCards, and Therapeutic Target Database (TTD) databases were applied to screen for vitiligo-related targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed through the Matascape database. Molecular docking and dynamics simulation methods were for the analysis of the binding sites and binding energies between the FHB's active components and the targets. Finally, a vitiligo mouse model was created, and the therapeutic effect and molecular mechanism of action of FHB were validated using enzyme linked immunosorbent assay (ELISA), western blot (WB), and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Additionally, hematoxylin-eosin staining (HE) and blood biochemical assays were conducted to assess the biosafety of FHB. The screening of chemical composition and targets suggested that 94 genetic targets of FHB were associated with vitiligo. The bioinformatics analysis suggested that luteolin, quercetin, and wogonin may be major active components, and nuclear factor-kappa B p65 subunit (RELA), signal transducer, and activator of transcription (STAT) 3 and RAC-alpha serine/threonine-protein kinase (AKT) 1 may be potential targets of FHB-vitiligo therapy. Molecular docking and dynamics simulation further demonstrated that luteolin, quercetin, and wogonin all bound best to STAT3. Through experimental verification, FHB has been demonstrated to alleviate the pathogenic characteristics of vitiligo mice, suppress the JAK-STAT signaling pathway, reduce inflammation, and increase melanogenesis. The in vivo safety evaluation experiments also demonstrated the non-toxicity of FHB. FHB exerts anti-inflammatory and melanogenesis-promoting effects via the effect of multi-component on multi-target, among which the JAK-STAT pathway is a validated FHB-vitiligo target, providing new ideas and clues for the development of vitiligo therapy.

Objectives: Coronavirus disease 2019 (COVID-19) has had a global impact and is spreading quickly. ChuanKeZhi injection (CKZI) is widely used in asthma patients. In this paper, we aimed to explore active compounds of CKZ and determine potential mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology, molecular docking and dynamic simulation studies. Materials and Methods: We used the Systematic Pharmacology Database and Analysis Platform of Traditional Chinese Medicine (TCMSP) to screen active compounds and potential target proteins of CKZ. COVID-19 target genes were screened via the American National Center for Biotechnology Information (NCBI) gene database and human gene database (GeenCards). The protein interaction network was constructed by the Protein Interaction Network Database (Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)) platform. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by the Metascape database. The main active compounds of CKZ were docked with angiotensin-converting enzyme 2 (ACE2), spike protein S1, and SARS-CoV-2-3CL pro and also docked with hub targets. We performed molecular dynamics (MD) simulation studies for validation. Results: We finally obtained 207 CKZ potential targets and 4681 potential COVID-19 targets. Key targets included mainly AKT1, TNF, IL6, VEGFA, IL1B, TP53, JUN, CASP3, etc. There were 217 Gene Ontology (GO) items in the GO enrichment analysis ( p < 0.05). The main KEGG pathways included the advanced glycation end products (AGE)- receptor for AGE (RAGE) signalling pathway in diabetic complications, rheumatoid arthritis, chemical carcinogenesis-receptor activation, alcoholic liver disease, etc. Molecular docking and dynamics simulation studies both exhibited great binding capacity. Conclusions: Network pharmacology, molecular docking and dynamics simulation studies were used to identify the potential and key targets, pharmacological functions, and therapeutic mechanisms of CKZI in the treatment of COVID-19. CKZI may be an effective and safe drug in COVID-19 treatment. However, further work is needed for validation.

This research delved into the molecular mechanism underlying dioctyl terephthalate (DOTP)-related periodontitis (PD) through the application of network toxicology, molecular docking, and molecular dynamics simulations. By leveraging data from SwissTargetPrediction, SuperPred, and GeneCards databases, targets associated with DOTP toxicity and PD were pinpointed, leading to the identification of 37 shared targets through a comprehensive analysis. Enrichment analysis unveiled significant implications in inflammatory responses (e.g., the AGE-RAGE signaling pathway) and immune regulatory pathways (e.g., the C-type lectin receptor pathway). Core targets (PTGS2, MAPK14, NFKB1, STAT1) were pinpointed utilizing Cytoscape and molecular docking techniques. DOTP exhibited robust binding to these targets through hydrogen bonding and hydrophobic interactions, with the DOTP-PTGS2 complex displaying the most favorable binding energy (− 7.1 kcal/mol). Molecular dynamics simulations validated the stability of this complex, demonstrating the lowest root mean square deviation (RMSD) of 0.22 nm and the largest buried solvent-accessible surface area (Buried SASA) of 12 nm2, indicating its superior stability. This investigation elucidates the molecular basis of DOTP-related PD, underscores the efficacy of network toxicology and computational modeling in environmental health risk assessment, and provides a theoretical framework for targeted interventions.

Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of −9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.

Simple SummaryCOPD is a lung disease characterized by limited respiratory airflow aggravated with time. JPYF II granules are a traditional Chinese medicine used in the treatment of COPD. However, the main components and potential mechanisms of JPYF II granules are still unclear. The purpose of this study was to elucidate the potential mechanism underlying its ability to treat COPD through network pharmacology, molecular docking, and molecular dynamics simulation techniques. Kaempferol, quercetin, and stigmasterol are the main active compounds in the JPYF II Formula in the treatment of COPD, and AKT1, IL-6, and TNF are potential target proteins for the JPYF II Formula in the treatment of COPD. The potential effective compounds, targets, and related potential molecular mechanisms obtained here provide a reference for follow-up studies on COPD.Background: JianPiYiFei (JPYF) II granules are a Chinese medicine for the treatment of chronic obstructive pulmonary disease (COPD). However, the main components and underlying mechanisms of JPYF II granules are not well understood. This study aimed to elucidate the potential mechanism of JPYF II granules in the treatment of COPD using network pharmacology, molecular docking, and molecular dynamics simulation techniques. Methods: The active compounds and corresponding protein targets of the JPYF II granules were found using the TCMSP, ETCM, and Uniport databases, and a compound–target network was constructed using Cytoscape3.9.1. The COPD targets were searched for in GEO datasets and the OMIM and GeneCards databases. The intersection between the effective compound-related targets and disease-related targets was obtained, PPI networks were constructed, and GO and KEGG enrichment analyses were performed. Then, molecular docking analysis verified the results obtained using network pharmacology. Finally, the protein–compound complexes obtained from the molecular docking analysis were simulated using molecular dynamics (MD) simulations. Results: The network pharmacological results showed that quercetin, kaempferol, and stigmasterol are the main active compounds in JPYF II granules, and AKT1, IL-6, and TNF are key target proteins. The PI3K/AKT signaling pathway is a potential pathway through which the JPYF II granules affect COPD. The results of the molecular docking analysis suggested that quercetin, kaempferol, and stigmasterol have a good binding affinity with AKT1, IL-6, and TNF. The MD simulation results showed that TNF has a good binding affinity with the compounds. Conclusions: This study identified the effective compounds, targets, and related underlying molecular mechanisms of JPYF II granules in the treatment of COPD through network pharmacology, molecular docking, and MD simulation techniques, which provides a reference for subsequent research on the treatment of COPD.

Discovery of novel Glutaminase allosteric inhibitors through drug repurposing and comparative MMGB/PBSA and molecular dynamics simulation

In this study, a series of NSAID 1-acyl-4-cycloalkyl/arylsemicarbazides and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazides possess anti-cancer activity against three human cancer cell lines L1210, CEM, and HeLa have been investigated using a combined approach including quantitative structure activity relationship (QSAR) study, molecular docking, and molecular dynamics (MD) simulations. First, different molecular descriptors were calculated for these compounds. Stepwise multiple linear regression (MLR) method was performed for selecting some common descriptors, including VEA1, hydration energy (HE), log P, and binding energy for all three cell lines. Then, other QSAR models were constructed using support vector regression (SVR) method. According to the results, SVR models were more efficient in predicting the anti-cancer activity. To better understand the mechanism of the binding interactions of NSAID 1-acyl-4-cycloalkyl/arylsemicarbazide and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazide derivatives with 5-lipoxygenase (5-LOX) protein, molecular docking studies were conducted. These studies have also been used to explore the effects of HE, log P, and binding energy on anti-cancer activity of the studied compounds. The results of molecular docking suggest that hydrophobic interactions of ligands with the active site of 5-LOX are responsible for their anti-cancer activities. Finally, molecular dynamics (MD) simulations using GROMACS package were used for evaluating the stability of 5-LOX in complex with NSAID 1-acyl-4-cycloalkyl/arylsemicarbazide and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazide derivatives. The results of MD simulations demonstrate stability of protein structure in complex with active compounds, 12 and 27.

BackgroundArtificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.MethodsTargets associated with commonly used AS were screened and validated using databases such as CTD, STITCH, Super-PRED, Swiss Target Prediction, SEA, PharmMapper, and GalaxySagittarius. Cancer-related targets were sourced from GeneCards, OMIM, and TTD databases. AS-cancer targets were identified through the intersection of these datasets. A network visualization (‘AS-targets-cancer’) was constructed using Cytoscape 3.9.0. Protein–protein interaction analysis was conducted using the STRING database to identify significant AS-cancer targets. GO and KEGG enrichment analyses were performed using the DAVID database. Core targets were identified from significant targets and genes involved in the ‘Pathways in cancer’ (map05200). Molecular docking and dynamics simulations were employed to verify interactions between AS and target proteins. Pan-cancer and univariate Cox regression analyses of core targets across 33 cancer types were conducted using GEPIA 2 and SangerBox, respectively. Gene chip datasets (GSE53757 for KIRC, GSE21354 for LGG, GSE42568 for BRCA, and GSE46602 for PRAD) were retrieved from the GEO database, while transcriptome and overall survival data were obtained from TCGA. Data normalization and identification of differentially expressed genes (DEGs) were performed on these datasets using R (version 4.3.2). Gene Set Enrichment Analysis (GSEA) was employed to identify critical pathways in the gene expression profiles between normal and cancer groups. A cancer risk prognostic model was constructed for key targets to further elucidate their significance in cancer initiation and progression. Finally, the HPA database was utilized to investigate variations in the expression of key AS-cancer target proteins across KIRC, LGG, BRCA, PRAD, and normal tissues.ResultsSeven commonly used AS (Aspartame, Acesulfame, Sucralose, NHDC, Cyclamate, Neotame, and Saccharin) were selected for study. A total of 368 AS-cancer intersection targets were identified, with 48 notable AS-cancer targets, including TP53, EGFR, SRC, PIK3R1, and EP300, retrieved. GO biological process analysis indicated that these targets are involved in the regulation of apoptosis, gene expression, and cell proliferation. Thirty-five core targets were identified from the intersection of the 48 significant AS-cancer targets and genes in the 'Pathways in cancer' (map05200). KEGG enrichment analysis of these core targets revealed associations with several cancer types and the PI3K-Akt signaling pathway. Molecular docking and dynamics simulations confirmed interactions between AS and these core targets. HSP90AA1 was found to be highly expressed across the 33 cancer types, while EGF showed the opposite trend. Univariate Cox regression analysis demonstrated strong associations of core targets with KIRC, LGG, BRCA, and PRAD. DEGs of AS-cancer core targets across these four cancers were analyzed. GSEA revealed upregulated and downregulated pathways enriched in KIRC, LGG, BRCA, and PRAD. Cancer risk prognostic models were constructed to elucidate the significant roles of key targets in cancer initiation and progression. Finally, the HPA database confirmed the crucial function of these targets in KIRC, LGG, BRCA, and PRAD.ConclusionThis study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. This paper provides a valuable reference for the safety assessment and cancer risk evaluation of food additives. It urges food safety regulatory agencies to strengthen oversight and encourages the public to reduce consumption of foods and beverages containing artificial sweeteners and other additives.

Vicenin-2 reduces inflammation and apoptosis to relieve skin photoaging via suppressing GSK3β.

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer. In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors. The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q2), noncross- validated correlation coefficient (r2ncv) and predicted correlation coefficient (r2pred) of the training and test set molecules, respectively. The CoMFA (q2, 0.670; r2ncv, 0.930; r2pred, 0.968), CoMFA-RF (q2, 0.694; r2ncr, 0.926; r2pred, 0.927), CoMSIA (q2, 0.834; r2ncv, 0.956; r2pred, 0.958) and HQSAR models (q2, 0.854; r2ncv, 0.900; r2pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings. These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

Si-Miao-Yong-An (SMYA) Decoction, a traditional Chinese herbal mixture, shows promise for managing diabetic complications. Up to this point, no reports have explored the effects of SMYA on diabetic wounds or the underlying mechanisms. This study aimed to investigate the therapeutic potential of SMYA in promoting diabetic wound healing and to elucidate the underlying molecular mechanisms. The wound healing effects of SMYA were evaluated in db/db diabetic mice by measuring wound closure rates and histological characteristics, including epidermal thickness and collagen deposition. Network pharmacology was utilized to identify active ingredients and corresponding therapeutic targets of SMYA, followed by validation through molecular docking and molecular dynamics simulations. KEGG and GO enrichment analyses were conducted to elucidate the relevant biological processes and pathways. In vitro studies involving high-glucose-treated HUVECs assessed the effects of SMYA-containing serum on cellular migration and angiogenesis. Finally, the expression of inflammatory factors and RAGE in the wound tissue was detected by qRT-PCR. SMYA significantly accelerated wound closure in db/db mice, as evidenced by improved epidermal thickness, tissue morphology, and collagen deposition. Network pharmacology identified 140 overlapping genes involved in angiogenesis and inflammation, with the AGE-RAGE signaling pathway playing a central role. Molecular docking and dynamics simulations revealed strong binding stability of quercetin and kaempferol to inflammation-related hub targets, including IL-6, TNF, and IL-1β. In vitro, SMYA-containing serum alleviated high-glucose-induced impairments in HUVEC migration and angiogenesis. Furthermore, qRT-PCR analysis showed that SMYA significantly downregulated Tnf, Il1b, Il6, and Rage expression in wound tissues, supporting its anti-inflammatory effect. SMYA promotes diabetic wound healing by modulating the inflammatory microenvironment and inhibiting the AGE-RAGE signaling pathway. These findings provide robust evidence for SMYA's therapeutic potential and lay a foundation for its future clinical application in treating diabetic wounds.

The advent of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) pandemic, has impacted physical and mental health worldwide. The lack of effective antiviral drugs necessitates a robust therapeutic approach to develop anti-SARS-CoV-2 drugs. Various investigations have recognized ACE2 as the primary receptor of SARS-CoV-2, and this amalgamation of ACE2 with the spike protein of the coronavirus is paramount for viral entry into the host cells and inducing infection. Consequently, restricting the virus's accessibility to ACE2 offers an alternative therapeutic approach to averting this illness. The study aimed to identify potent inhibitors with enhanced affinity for the ACE2 protein and validate their stability and efficacy against established inhibitors via molecular docking, machine learning, and MD simulations. 202 ACE2 inhibitors (PDB ID and 6LZG), comprising repurposed antiviral compounds and specific ACE2 inhibitors, were selected for molecular docking. The two most effective compounds obtained from docking were further analyzed using machine learning to identify potential compounds with enhanced ACE2-binding affinity. To refine the dataset, molecular decoys were generated through the Database of Useful Decoys: Enhanced (DUD-E) server, and Singular Value Decomposition (SVD) was applied for data preprocessing. The Tree-based Pipeline Optimization Tool (TPOT) was then utilized to optimize the machine learning pipeline. The most promising ML-predicted compounds were re-evaluated through docking and subjected to Molecular Dynamics (MD) simulations to evaluate their structural stability and interactions with ACE2. Finally, these compounds were evaluated against the top two pre-established inhibitors using various computational tools. The two best pre-established inhibitors were identified as Birinapant and Elbasvir, while the best machine-learning-predicted compounds were PubChem ID: 23658468 and PubChem ID: 117637105. Pharmacophore studies were conducted on the most effective machine-learning-predicted compounds, followed by a comparative ADME/T analysis between the best ML-screened and pre-established inhibitors. The results indicated that the top ML compound (PubChem ID: 23658468) demonstrated favorable BBB permeability and a high HIA index, highlighting its potential for therapeutic applications. The ML-screened ligand demonstrated structural stability with an RMSD (0.24 nm) and greater global stability (Rg: 2.08 nm) than Birinapant. Hydrogen bonding interactions further validated their strong binding affinity. MM/PBSA analysis confirmed the ML-screened compound's stronger binding affinity, with a binding free energy of - 132.90 kcal/mol, indicating enhanced stability in complex formation. The results emphasize the efficacy of integrating molecular docking, machine learning, and molecular dynamics simulations in facilitating the rapid identification of novel inhibitors. PubChem ID: 23658468 demonstrates robust binding affinity to ACE2 and favorable pharmacokinetic properties, establishing it as a promising candidate for further investigation.

Dissecting the molecular mechanism of cepharanthine against COVID-19, based on a network pharmacology strategy combined with RNA-sequencing analysis, molecular docking, and molecular dynamics simulation