Current computer-aided drug design最新文献

Aims: To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data.

Background: Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown.

Methods: Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis.

Results: We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecula

Background: Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained.

Objective: The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches.

Methods: Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ _{Lin_F9}XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD).

Results: Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG.

Conclusion: Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Background: In vitro, the molecular docking method has been suggested for estimating the biological affinity of the pharmacophores with physiologically active compounds. It is the latter stage in molecular docking, and the docking scores are examined using the AutoDock 4.2 tool program. The chosen compounds can be evaluated for in vitro activity based on the binding scores, and the IC₅₀ values can be computed.

Objective: The purpose of this work was to create methyl isatin compounds as potential antidepressants, compute physicochemical characteristics, and carry out docking analysis.

Methods: The protein data bank of the RCSB (Research Collaboratory for Structural Bioinformatics) was used to download the PDB structures of monoamine oxidase (PDB ID: 2BXR) and indoleamine 2,3-dioxygenase (PDB ID: 6E35). Based on the literature, methyl isatin derivatives were chosen as the lead chemicals. By determining their IC₅₀ values, the chosen compounds were tested for in vitro anti-depressant activity.

Results: The binding scores for the interactions of SDI 1 and SD 2 with indoleamine 2,3 dioxygenase were found to be -10.55 kcal/mol and -11.08 kcal/mol, respectively, while the scores for their interactions with monoamine oxidase were found to be -8.76 kcal/mol and -9.28 kcal/mol, respectively, using AutoDock 4.2. The relationship between biological affinity and pharmacophore electrical structure was examined using the docking technique. The chosen compounds were tested for their ability to inhibit MAO, and the IC₅₀ values for each were found to be 51.20 and 56, respectively.

Conclusion: This investigation has identified many novel and effective MAO-A inhibitors from the family of chemicals known as methyl isatin derivatives. Lead optimization was applied to the SDI 1 and SDI 2 derivatives. The superior bioactivity, pharmacokinetic profile, BBB penetration, pre-ADMET profiles, such as HIA (human intestinal absorption) and MDCK (Madin-Darby canine kidney), plasma protein binding, toxicity assessment, and docking outcomes, have been obtained. According to the study, synthesised isatin 1 and SDI 2 derivatives exhibited a stronger MAO inhibitory activity and effective binding energy, which may help prevent stress-induced depression and other neurodegenerative disorders caused by a monoamine imbalance.

Aims and objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS.

Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs.

Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS.

Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Background: Leptin is predominant in regulating body weight by stimulating energy expenditure through its neuronal action in the brain. Moreover, it is projected to adipose tissue and induces adipocyte browning by activating the β3-adrenergic receptor (β3AR). However, the expression of leptin receptor (Lep-R) and β3AR in people with obesity is downregulated.

Aim: We hypothesized that Hibiscus sabdariffa Linn. extract (HSE) would increase hypothalamus arcuate nucleus (ARC) Lep-R and white adipose tissue (WAT) β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as activators of Lep-R and β3AR by an in-silico experiment.

Methods: Twenty-four male Sprague-Dawley rats were divided into four groups: Control (standard food), DIO (high-fat diet), DIO-Hib200 (HFD+HSE 200 mg/kg BW), and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE was administered orally for five weeks, once a day.

Results: HSE administration significantly (p <0,05) increased the ARC Lep-R expression. The Lee index significantly decreased to the normal range (≤ 310) with p <0,001 for DIO-Hib200 and p <0,01 for DIO-Hib400. Among 39 bioactive compounds, 5-O-caffeoyl shikimic acid exhibited high free binding scores (-8,63) for Lep-R, and myricetin_3_arabinogalactoside had high free binding scores (-9,39) for β3AR. These binding predictions could activate Lep-R and β3AR.

Conclusion: This study highlights that HSE could be a potential therapeutic target for obesity by increasing LepR mRNA and leptin sensitivity, enhancing energy expenditure, and reducing obesity.

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.

Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.

Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.

Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.

Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Introduction: Sepsis is a state of the systemic inflammatory response of the host induced by infection, frequently affecting numerous organs and producing varied degrees of damage. The most typical consequence of sepsis is sepsis-associated acute kidney injury(SA-AKI). Xuebijing is developed based on XueFuZhuYu Decoction. Five Chinese herbal extracts, including Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix, make up the majority of the mixture. It has properties that are anti-inflammatory and anti-oxidative stress. Xuebijing is an effective medication for the treatment of SA-AKI, according to clinical research. But its pharmacological mechanism is still not completely understood.

Methods: First, the composition and target information of Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix were collected from the TCMSP database, while the therapeutic targets of SA-AKI were exported from the gene card database. To do a GO and KEGG enrichment analysis, we first screened the key targets using a Venn diagram and Cytoscape 3.9.1. To assess the binding activity between the active component and the target, we lastly used molecular docking.

Results: For Xuebijing, a total of 59 active components and 267 corresponding targets were discovered, while for SA-AKI, a total of 1,276 targets were connected. There were 117 targets in all that was shared by goals for active ingredients and objectives for diseases. The TNF signaling pathway and the AGE-RAGE pathway were later found to be significant pathways for the therapeutic effects of Xuebijing by GO analysis and KEGG pathway analysis. Quercetin, luteolin, and kaempferol were shown to target and modulate CXCL8, CASP3, and TNF, respectively, according to molecular docking results.

Conclusion: This study predicts the mechanism of action of the active ingredients of Xuebijing in the treatment of SA-AKI, which provides a basis for future applications of Xuebijing and studies targeting the mechanism.