Current computer-aided drug design最新文献

Background: Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery.

Objective: In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus.

Methods: Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions.

Results: According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies.

Conclusion: Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.

Introduction: The conventional processes of drug discovery are too expensive, timeconsuming and the success rate is limited. Searching for alternatives that have evident safety and potential efficacy could save money, time and improve the current therapeutic regimen outcomes.

Methods: Clinical phytotherapy implies the use of extracts of natural origin for prophylaxis, treatment, or management of human disorders. In this work, the potential role of common Fig (Ficus carica) in the management of COVID-19 infections has been explored. The antiviral effects of Cyanidin 3-rhamnoglucoside which is abundant in common Figs have been illustrated on COVID-19 targets. The immunomodulatory effect and the ability to ameliorate the cytokine storm associated with coronavirus infections have also been highlighted. This work involves various computational studies to investigate the potential roles of common figs in the management of COVID-19 viral infections.

Results: Two molecular docking studies of all active ingredients in common Figs were conducted starting with MOE to provide initial insights, followed by Autodock Vina for further confirmation of the results of the top five compounds with the best docking score.

Conclusion: Finally, Molecular dynamic simulation alongside MMPBSA calculations were conducted using GROMACS to endorse and validate the entire work.

Objective: To explore the mechanism of Maiwei Dihuang decoction in the treatment of non-small cell lung cancer (NSCLC) by using network pharmacology and LC-MS technology.

Methods: The effective components in Maiwei Dihuang decoction were detected by liquid chromatography- mass spectrometry (LC-MS). Use the SuperPred database to collect the relevant targets of the active ingredients of Mai Wei Di Tang, and then collect the relevant targets of nonsmall cell lung cancer from GeneCards, DisgenNET and OMIM databases. On this basis, PPI network construction, GO enrichment analysis and KEGG pathway annotation analysis were carried out for target sites. Finally, AutoDock Vina is used for molecular docking.

Results: We further screened 16 effective Chinese herbal compounds through LC-MS combined with ADME level. On this basis, we obtained 77 core targets through protein interaction network analysis. Through GO, KEGG analysis and molecular docking results, we finally screened out the potential targets of Maiwei Dihuang Decoction for NSCLC: TP53, STAT3, MAPK3.

Conclusion: Maiwei Dihuang decoction may play a role in the treatment of NSCLC by coregulating TP53/STAT3/MAPK3 signal pathway.

Background: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.

Objectives: The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues.

Methods: The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy.

Results: ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC₅₀ value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity.

Conclusion: The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Background: Studies have indicated that Sinomenii Caulis (SC) has several physiological activities, such as anti-inflammatory, anti-cancer, immunosuppression, and so on. SC is currently widely used in the treatment of rheumatoid arthritis, skin disease, and other diseases. However, the mechanism of SC in the treatment of ulcerative colitis (UC) remains unclear.

Aims: To predict the active components of SC and determine the mechanism of SC on UC.

Methods: Active components and targets of SC were screened and obtained by TCMSP, PharmMapper, and CTD databases. The target genes of UC were searched from GEO (GSE9452), and DisGeNET databases. Based on the String database, Cytoscape 3.7.2 software, and David 6.7 database, we analyzed the relationship between SC active components and UC potential targets or pathways. Finally, identification of SC targets in anti-UC by molecular docking. GROMACS software was used to perform molecular dynamics simulations of protein and compound complexes and to perform free energy calculations.

Results: Six main active components, 61 potential anti-UC gene targets, and the top 5 targets with degree value are IL6, TNF, IL1β, CASP3, and SRC. According to GO enrichment analysis, the vascular endothelial growth factor receptor and vascular endothelial growth factor stimulus may be relevant biological processes implicated in the treatment of UC by SC. The KEGG pathway analysis result was mainly associated with the IL-17, AGE-RAGE, and TNF signaling pathways. Based on molecular docking results, beta-sitosterol, 16-epi-Isositsirikine, Sinomenine, and Stepholidine are strongly bound to the main targets. Molecular dynamics simulation results showed that IL1B/beta-sitosterol and TNF/16-epi-Isositsirikine binding was more stable.

Conclusion: SC can play a therapeutic role in UC through multiple components, targets, and pathways. The specific mechanism of action needs to be further explored.

Objective: This study aimed to analyze the potential targets and mechanism of the Tiannanxing-shengjiang drug pair in pain treatment using network pharmacology and molecular docking technology.

Methods: The active components and target proteins of Tiannanxing-Shengjiang were obtained from the TCMSP database. The pain-related genes were acquired from the DisGeNET database. The common target genes between Tiannanxing-Shengjiang and pain were identified and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses on the DAVID website. AutoDockTools and molecular dynamics simulation analysis were used to assess the binding of the components with the target proteins.

Results: Ten active components were screened out, such as stigmasterol, β-sitosterol, and dihydrocapsaicin. A total of 63 common targets between the drug and pain were identified. GO analysis showed the targets to be mainly associated with biological processes, such as inflammatory response and forward regulation of the EKR1 and EKR2 cascade. KEGG analysis revealed 53 enriched pathways, including pain-related calcium signaling, cholinergic synaptic signaling, and serotonergic pathway. Five compounds and 7 target proteins showed good binding affinities. These data suggest that Tiannanxing-shengjiang may alleviate pain through specific targets and signaling pathways.

Conclusion: The active ingredients in Tiannanxing-shengjiang might alleviate pain by regulating genes, such as CNR1, ESR1, MAPK3, CYP3A4, JUN, and HDAC1 through the signaling pathways, including intracellular calcium ion conduction, cholinergic prominent signaling, and cancer signaling pathway.

Background: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage.

Objective: The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer.

Methods: Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC.

Results: Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer.

Conclusion: This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

Introduction: Agricultural chemicals are impacting health nowadays. Recently, promising synergistic antifungal interaction between tacrolimus and some azole compounds was studied.

Objective: To determine ADME parameters, potential side effects of test substances to reduce time and resources in the future.

Methods: All descriptors and molecular parameters were obtained by the protocols of SwissADME and ProTox II.

Results: In the result, the following physicochemical and drug-likeness parameters were calculated.

Conclusion: Studied triazoles 1 and 2 showed good ADME characteristics and promising toxicity levels suitable to be checked for in vitro toxicology in case of future advanced results in the agricultural field.

Background: Bladder cancer (BCa) is the most common malignancy of the urinary system. Inflammation is critical in the occurrence and development of BCa. The purpose of this study was to identify key genes and pathways of inflammatory bowel disease in BCa through text mining technology and bioinformatics technology and to explore potential therapeutic drugs for BCa.

Methods: Genes associated with BCa and Crohn's disease (CD) were detected using the text mining tool GenClip3, and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, and modular analysis was performed using the Molecular Complex Detection plugin (MCODE). Finally, the genes clustered in the first two modules were selected as core genes, and the drug-gene interaction database was used to discover potential therapeutic drugs.

Results: We identified 796 genes shared by "Bladder cancer" and "Crohn's disease" by text mining. Gene function enrichment analysis yielded 18 enriched GO terms and the 6 most relevant KEGG pathways. A PPI network with 758 nodes and 4014 edges was constructed, and 20 gene modules were obtained using MCODE. We selected the top two gene clusters as core candidate genes. We found that 3 out of 55 selected core genes could be targeted by 26 existing drugs.

Conclusion: The results indicated that CXCL12, FGF2 and FSCN1 are potential key genes involved in CD with BCa. Additionally, 26 drugs were identified as potential therapeutics for BCa treatment and management.

Background: Discovery of novel antimicrobial agents is in need to deal with antibiotic resistance. Elucidating the mechanism of action for established drugs contributes to this endeavor. DNA gyrase is a therapeutic target used in the design and development of new antibacterial agents. Selective antibacterial gyrase inhibitors are available; however, resistance development against them is a big challenge. Hence, novel gyrase inhibitors with novel mechanisms are required.

Objective: The aim of this study is to elucidate mode of action for existing DNA gyrase inhibitors and to pave the way towards discovery of novel inhibitors.

Methods: In this study, the mechanism of action for selected DNA gyrase inhibitors available was carried out through molecular docking and molecular dynamics (MD) simulation. In addition, pharmacophore analysis, density functional theory (DFT) calculations, and computational pharmacokinetics analysis of the gyrase inhibitors were performed.

Results: This study demonstrated that all the DNA gyrase inhibitors investigated, except compound 14, exhibit their activity by inhibiting gyrase B at a binding pocket. The interaction of the inhibitors at Lys103 was found to be essential for the binding. The molecular docking and MD simulation results revealed that compound 14 could act by inhibiting gyrase A. A pharmacophore model that consisted of the features that would help the inhibition effect was generated. The DFT analysis demonstrated 14 had relatively high chemical stability. Computational pharmacokinetics analysis revealed that most of the explored inhibitors were estimated to have good drug-like properties. Furthermore, most of the inhibitors were found to be non-mutagenic.

Conclusion: In this study, mode of action elucidation through molecular docking and MD simulation, pharmacophore model generation, pharmacokinetic property prediction, and DFT study for selected DNA gyrase inhibitors were carried out. The outcomes of this study are anticipated to contribute to the design of novel gyrase inhibitors.