In silico design of enzyme α-amylase and α-glucosidase inhibitors using molecular docking, molecular dynamic, conceptual DFT investigation and pharmacophore modelling

Background/Objectives:Momordica charantia L. (M. charantia), a widely cultivated and frequently consumed medicinal plant, is utilized in traditional medicine. Cucurbitane-type triterpenoids, significant saponin components of M. charantia, exhibit hypoglycemic effects; however, the underlying mechanisms remain unclear. Methods: This study utilized comprehensive network pharmacology to identify potential components of M. charantia cucurbitane-type triterpenoids that may influence type 2 diabetes mellitus (T2DM). Additionally, molecular docking and molecular dynamics studies were performed to assess the stability of the interactions between the selected components and key targets. Results: In total, 22 candidate active components of M. charantia cucurbitane-type triterpenoids and 1165 disease targets for T2DM were identified through database screening. Molecular docking and molecular dynamics simulations were conducted for five key components (Kuguacin J, 25-O-methylkaravilagenin D, Momordicine I, momordic acid, and Kuguacin S) and three key targets (AKT1, IL6, and SRC), and the results demonstrated stable binding. The experimental results indicate that the interactions between momordic acid-AKT1 and momordic acid-IL6 are stable. Conclusions: Momordic acid may play a crucial role in M. charantia's regulation of T2DM, and AKT1 and IL6 seem to be key targets for the therapeutic action of M. charantia in managing T2DM.

Periodontitis (PD) and type 2 diabetes mellitus (T2DM) represent interlinked global health burdens, commonly causing significant clinical complications when coincident. Therefore, managing both conditions (T2DM with periodontitis, DP) simultaneously poses considerable challenges, necessitating novel therapeutic strategies. KQJF has been clinically proven to treat DP with good efficacy, but its pharmacological substances and targets are not clear and urgently need to be clarified. To define the potential active components and targets of KQJF for the treatment of DP. The investigation commenced with the application of UPLC-Q-TOF/MS analysis to delineate the active constituents of KQJF and their associated targets in addressing DP. Additionally, the research incorporated subsequent methodologies such as machine learning, network pharmacology, molecular docking, molecular dynamics simulations, and a DP rat model was established and validated by in vivo experiments using H&E staining, immunohistochemistry, quantitative real-time PCR, and Western blot. KQJF was found to contain 49 prototype compounds and 121 metabolites with potential activity against PD and T2DM. Network pharmacology revealed 66 overlapping genes between the pharmacological targets of KQJF and known targets of PD and T2DM. Further exploration through PPI network and enrichment analyses illuminated the involvement of multi-target and multi-pathway mechanisms. Molecular docking and dynamics simulations confirmed the robust interactions between key compounds within KQJF and proteins associated with the diseases. In vivo validation demonstrated that KQJF treatment ameliorated DP-associated histopathological changes and modulated the expression of crucial proteins (including ABCG2, CCND1, CDKN1B, HIF1A, and PIK3R1) in a DP rat model. In summary, KQJF exhibits potential therapeutic benefits for DP through a multi-component and multi-target approach, potentially offering a novel integrative treatment strategy. This study underscores the importance of integrating traditional medicine with modern molecular techniques to explore novel therapeutic avenues for complex comorbid conditions, providing a blueprint for future pharmacological explorations.

ObjectiveAnemarrhenae Rhizoma (AR) is a traditional Chinese medicine widely used for the treatment of type 2 diabetes mellitus (T2DM). However, the specific bioactive constituents responsible for its in vivo effects and their underlying mechanisms of action remain unclear. We hypothesise that serum-absorbed and metabolised AR components modulate key metabolic and inflammatory pathways in T2DM. To test this hypothesis, this study employs an integrated strategy combining metabolomics with serum-urine pharmacochemistry and network pharmacology to systematically identify AR’s active constituents and elucidate their multi-target mechanisms in T2DM management.MethodsUHPLC-Q-TOF-MS coupled with multivariate statistical analysis was employed to identify the AR-derived constituents in serum and urine of T2DM rats. Network pharmacology was utilised to predict the targets of the AR’s active components, while biochemical assays, liver histopathology, and metabolomics were performed to evaluate its therapeutic effects. Molecular docking and molecular dynamics (MD) simulations were conducted to assess the binding affinities between key components and their targets.Results77 AR components were identified, among which 47 prototypes and 11 metabolites were detected in serum and urine. The key bioactive constituents included sarsasapogenin, markogenin/neogitogenin, digitogenin, norathyriol, and mangiferin. AR treatment significantly reduced blood glucose and lipid levels, ameliorated insulin resistance, attenuated inflammation, and modulated the PPAR and NF-κB signalling pathways. Serum metabolomics analysis revealed 35 differential metabolites, with linoleic acid metabolism and PPAR signalling identified as the predominant metabolic pathways. Molecular docking and MD simulations demonstrated strong binding affinity between core components and key targets (PPARA, NFKB1, IL6, AKT1, IL1B). Pharmacological validation confirmed AR’s therapeutic efficacy in T2DM through regulation of these core targets.ConclusionAR ameliorates T2DM by suppressing NF-κB signalling and activating PPAR pathways, thereby improving metabolic dysregulation.

Dengue is a fast spreading mosquito borne viral disease that poses a serious threat to human health. Lack of therapeutic drugs and vaccines signify that more resources need to be explored. Accumulated evidence has suggested that plants offer a vast reservoir for antiviral drug discovery which are safe for human consumption. Plant-based drug discovery is a complex and time-consuming process as plants possess rich repository of chemically diverse compounds. Various in silico methods can make this process simple and economic. We, therefore, performed pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations and ADME (absorption, distribution, metabolism, excretion) prediction to screen potential candidates against dengue. In particular, combined pharmacophore mapping and molecular docking were used to prioritize the potentially active ligands from a ligand library. Biological activities of plant based ligands were predicted using 3D-QSAR pharmacophore modeling. Interaction between proteins, namely, envelope G protein, NS2B/NS3 protease, NS5 methyltransferase, NS1, NS5 polymerase and active plant-based ligands (pIC50 > 5.1) were analyzed using molecular docking. Best docked complex, namely, envelope G protein–mulberroside C, NS2B-NS3 protease–curcumin, NS5 methyltransferase–chebulic acid, NS1–mulberroside A, NS5 methyltransferase–punigluconin and NS5 methyltransferase–chebulic acid were further subjected to MD simulations study to assess the fluctuation and conformational changes during protein–ligand interaction. ADME studies were performed to assess their drug-likeness properties. Collectively, these in silico results helped to identify the potential plant-based hits against the various receptors of dengue virus which can be further validated by bioactivity-based experiments.Communicated by Ramaswamy H. Sarma

Type 1 diabetes mellitus (T1DM) is a metabolic disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in T1DM through adopting integrated bioinformatics tools. The gene expression Omnibus (GEO) database was used to obtain next generation sequencing data (GSE270484) of T1DM and normal control samples. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R bioconductor package. Gene Ontology (GO) and pathway enrichment analyses were performed by g:Profiler. The protein-protein interaction (PPI) network was plotted with IID PPI database and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC. Then, microRNAs (miRNAs) and transcription factors (TFs) in T1DM were screened out from the miRNet and NetworkAnalyst database. Then, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Moreover, a drug-hub gene interaction network of the hub genes was constructed and predicted the drug molecule against hub genes. The receiver operating characteristic (ROC) curves were generated to predict diagnostic value of hub genes. Finally we performed molecular docking, ADMET profiling and molecular dynamics simulation studies of marine derived chemical constituents using Schrodinger Suite 2025-1. A total of 958 DEGs were screened: 479 up regulated genes and 479 down regulated genes. DEG were mainly enriched in the terms of developmental process, membrane, cation binding, response to stimulus, cell periphery, ion binding, neuronal system and metabolism. Based on the data of protein-protein interaction (PPI), the top 10 hub genes (5 up regulated and 5 down regulated) were ranked, including FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6. The miRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-657, hsa-miR-1266-5p, NOTCH1 and GTF3C2 might play an important role in the pathogenesis of T1DM. The drug-hub gene interaction network showed that Clenbuterol, Diethylstilbestrol, Selegiline and Isoflurophate predicted therapeutic drugs for the T1DM. Molecular docking and molecular dynamics simulation study revealed that CMNPD5805 and CMNPD30286 as potential inhibitors of FN1 (pdb id: 3M7P) a key biomarker in pathogenesis of T1DM. These findings promote the understanding of the molecular mechanism and clinically related molecular targets for T1DM.

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.

New insights into the pharmacological mechanisms of Jinqi Jiangtang Tablets in the treatment of type 2 diabetes mellitus: A multi-omics approach combined with experimental validation.

Hypoxia-inducible factor (HIF) is a transcriptional factor which plays a crucial role in tumour metastasis thereby responsible for development of various forms of cancers. Indazole derivatives have been reported in the literature as potent HIF-1α inhibitor via interaction with key residues of the HIF-1α active site. Taking into consideration the role HIF-1α in cancer and potency of indazole derivative against HIF-1α; it was considered of interest to correlate structural features of known indazole derivatives with specified HIF-1α inhibitory activity to map pharmacophoric features through Three-dimensional quantitative structural activity relationship (3D-QSAR) and pharmacophore mapping. Field and Gaussian based 3D-QSAR studies were performed to realize the variables influencing the inhibitory potency of HIF-1α inhibitors. Field and Gaussian- based 3D-QSAR models were validated through various statistical measures generated by partial least square (PLS). The steric and electrostatic maps generated for both 3D-QSAR provide a structural framework for designing new inhibitors. Further; 3D-maps were also helpful in understanding variability in the activity of the compounds. Pharmacophore mapping also generates a common five-point pharmacophore hypothesis (A1D2R3R4R5_4) which can be employed in combination with 3D-contour maps to design potent HIF-1α inhibitors. Molecular docking and molecular dynamics (MD) simulation of the most potent compound 39 showed good binding efficiency and was found to be quite stable in the active site of the HIF-1α protein. The developed 3D-QSAR models; pharmacophore modelling; molecular docking studies along with the MD simulation analysis may be employed to design lead molecule as selective HIF-1α inhibitors for the treatment of Cancer.

Molecular modeling approach in design of new scaffold of α-glucosidase inhibitor as antidiabetic drug.

α-Glucosidase inhibitors (AGIs) are pharmacological agents commonly used to manage postprandial hyperglycemia associated with type 2 diabetes mellitus (T2DM). Developing novel, potent AGIs remains a significant area of research. In this study, we investigated a series of derivatives of the natural product from α-mangostin as potential AGIs. A combined experimental and computational approach was employed to characterize promising compounds with potent α-glucosidase inhibitory activity. We found that α-mangostin (AM) and its derivatives (AM1 - 3) exhibited micromolar range α-glucosidase inhibition (IC50 ranging from 15.14 to 67.81µM), surpassing the known drug acarbose (IC50 of 197.09µM). Among the derivatives, AM1 exhibited the most promising α-glucosidase inhibition, displaying competitive inhibition kinetics with a Ki value of 47.04µM. Molecular docking and molecular dynamics (MD) simulations provided mechanistic insights into the binding interactions between AM1 and the α-glucosidase active site. AM1 was observed to form hydrogen bonds and hydrophobic interactions with key amino acid residues within the enzyme's active site. The introduction of amine groups in compound AM1 enhanced activity compared to AM, the parent compound. This study highlights the potential of α-mangostin derivatives as potent AGIs. The identified lead compound, AM1, warrants further investigation to assess its efficacy and safety in managing T2DM.

In this study, we searched for potential DNA GyrB inhibitors using pharmacophore-based virtual screening followed by molecular docking and molecular dynamics simulation approaches. For this purpose, a set of 248 DNA GyrB inhibitors was collected from the literature and a well-validated pharmacophore model was generated. The best pharmacophore model explained that two each of hydrogen bond acceptors and hydrophobicity regions were critical for inhibition of DNA GyrB. Good statistical results of the pharmacophore model indicated that the model was robust in nature. Virtual screening of molecular databases revealed three molecules as potential antimycobacterial agents. The final screened promising compounds were evaluated in molecular docking and molecular dynamics simulation studies. In the molecular dynamics studies, RMSD and RMSF values undoubtedly explained that the screened compounds formed stable complexes with DNA GyrB. Therefore, it can be concluded that the compounds identified may have potential for the treatment of TB.

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.

Background: In the past decade, several antibodies directed against the PD-1/PD-L1 interaction have been approved. However, therapeutic antibodies also exhibit some shortcomings. Using small molecules to regulate the PD-1/PD-L1 pathway may be another way to mobilize the immune system to fight cancer. Method: 52,765 marine natural products were screened against PD-L1(PDBID: 6R3K). To identify natural compounds, a structure-based pharmacophore model was generated, following by virtual screening and molecular docking. Then, the absorption, distribution, metabolism, and excretion (ADME) test was carried out to select the most suitable compounds. Finally, molecular dynamics simulation was also performed to validate the binding property of the top compound. Results: Initially, 12 small marine molecules were screened based on the pharmacophore model. Then, two compounds were selected for further evaluation based on the molecular docking scores. After ADME and toxicity studies, molecule 51320 was selected for further verification. By molecular dynamics analysis, molecule 51320 maintains a stable conformation with the target protein, so it has the chance to become an inhibitor of PD-L1. Conclusions: Through structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET approaches, and molecular dynamics (MD) simulation, the marine natural compound 51320 can be used as a small molecule inhibitor of PD-L1.

Jinlida granule (JLD) is a Traditional Chinese Medicine (TCM) formula used for the treatment of type 2 diabetes mellitus (T2DM). However, the mechanism of JLD treatment for T2DM is not fully revealed. In this study, we explored the mechanism of JLD against T2DM by an integrative pharmacology strategy. Active components and corresponding targets were retrieved from Traditional Chinese Medicine System Pharmacology (TCMSP), SwissADME and Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine Database (BATMAN-TCM) database. T2DM-related targets were obtained from Drugbank and Genecards databases. The protein–protein interaction (PPI) network was constructed and analyzed with STRING (Search Toll for the Retrieval of Interacting Genes/proteins) and Cytoscape to get the key targets. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID). Lastly, the binding capacities and reliability between potential active components and the targets were verified with molecular docking and molecular dynamics simulation. In total, 185 active components and 337 targets of JLD were obtained. 317 targets overlapped with T2DM-related targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), interleukin-6 (IL-6), cellular tumor antigen p53 (TP53), prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3) and signal transducer and activator of transcription 3 (STAT3) were identified as seven key targets by the topological analysis of the PPI network. GO and KEGG enrichment analyses showed that the effects were primarily associated with gene expression, signal transduction, apoptosis and inflammation. The pathways were mainly enriched in PI3K-AKT signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Molecular docking and molecular dynamics simulation verified the good binding affinity between the key components and targets. The predicted results may provide a theoretical basis for drug screening of JLD and a new insight for the therapeutic effect of JLD on T2DM.

Benign prostatic hyperplasia (BPH) is caused by augmented levels of androgen dihydrotestosterone (DHT) which is involved in the growth of the prostate in humans. 5α-Reductase type II (5αR2) is an intracellular enzyme that catalyses the formation of DHT from testosterone; hence, the inhibition of 5αR2 has emerged as one of the most promising strategies for the treatment of BPH. In this study, a computational approach that integrates ligand-based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics (MD) simulations was adopted to discover novel 5αR2 inhibitors with less side effects. After validating by Fischer's randomisation and Güner–Henry test, the best quantitative pharmacophore model (Hypo1), consisting of two hydrogen-bond acceptors and three hydrophobic features, was subsequently used as a three-dimensional-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute databases. These hits were further filtered by ADMET (absorption, distribution, metabolism, elimination and toxicology) and molecular docking experiments, and their binding stabilities were validated by 10-ns MD simulations. Finally, only one hit was identified as a potential lead based on higher predicted inhibitory activity to 5αR2 compared with the most active inhibitor (finasteride). Our results further suggest that this potential lead could easily be synthesised and has structural novelty, making it a promising candidate for treating BPH.