Current computer-aided drug design最新文献

Background: Receptor tyrosine kinases (RTKs) are potent oncoproteins in cancer that, when mutated or overexpressed, can cause uncontrolled growth of cells, angiogenesis, and metastasis, making them significant targets for cancer treatment. Vascular endothelial growth factor receptor 2 (VEGFR2), is a tyrosine kinase receptor that is produced in endothelial cells and is the most crucial regulator of angiogenic factors involved in tumor angiogenesis. So, a series of new substituted N-(4-((2-aminopyrimidin-5-yl)oxy)phenyl)-N-phenyl cyclopropane1,1-dicarboxamide derivatives as VEGFR-2 inhibitors have been designed and synthesized.

Methods: Utilizing H-NMR, C13-NMR, and mass spectroscopy, the proposed derivatives were produced and assessed. HT-29 and COLO-205 cell lines were used for the cytotoxicity tests. The effective compound was investigated further for the Vegfr-2 kinase inhibition assay, cell cycle arrest, and apoptosis. A molecular docking examination was also carried out with the Maestro-12.5v of Schrodinger.

Results: In comparison to the reference drug Cabozantinib (IC50 = 9.10 and 10.66 µM), compound SP2 revealed promising cytotoxic activity (IC50 = 4.07 and 4.98 µM) against HT-29 and COLO-205, respectively. The synthesized compound SP2 showed VEGFR-2 kinase inhibition activity with (IC50 = 6.82 µM) against the reference drug, Cabozantinib (IC50 = 0.045 µM). Moreover, compound SP2 strongly induced apoptosis by arresting the cell cycle in the G1 phase. The new compounds' potent VEGFR-2 inhibitory effect was noted with key amino acids Asp1044, and Glu883, and the hydrophobic interaction was also observed in the pocket of the VEGFR-2 active site by using a docking study.

Conclusion: The results demonstrate that at the cellular and enzyme levels, the synthetic compounds SP2 are similarly effective as cabozantinib. The cell cycle and apoptosis data demonstrate the effectiveness of the suggested compounds. Based on the findings of docking studies, cytotoxic effects, in vitro VEGFR-2 inhibition, apoptosis, and cell cycle arrest, this research has given us identical or more effective VEGFR-2 inhibitors.

Background: SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest.

Objective: This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations.

Methods: An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted.

Results: Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors.

Conclusion: The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

Introduction: Inflammatory bowel disease (IBD) has become one of the public problems worldwide and its incidence rate is increasing year by year. Its concomitant disease i.e. diabetes mellitus (DM) has attracted more and more attention due to DM altering the progression of IBD and leading to long periods of intermittent recurrence and deterioration. The common mechanism and potential target drug of IBD with comorbid chronic conditions of DM were explored.

Methods: Gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were identified by R software. GO annotation and pathway enrichment were performed, a protein-protein interaction (PPI) network was constructed, associated lncRNAs were predicted and drug prediction targeting key genes was made. Additionally, the regulatory network among core genes, associated pathways, and predicted lncRNA in IBD with coexistent DM were visualized.

Results: We identified the critical gene MMP3 with lncRNA CDKN2BAS involved in the PPAR pathway, which uncovered the underlying regulatory mechanism of IBD with coexistent DM. We also predicted the potential therapeutic compound ZINC05905909 acting on MMP3.

Conclusion: Our findings revealed the regulatory mechanism chain of critical gene MMP3, lncRNA CDKN2BAS, and PPAR pathway and provided potential therapeutic compound ZINC05905909 for drug therapy to treat comorbid IBD DM.

Background: Uncaria tomentosa is a traditional medicinal herb renowned for its anti-inflammatory, antioxidant, and immune-enhancing properties. In the realm of neurodegenerative diseases (NDDS), CLASP proteins, responsible for regulating microtubule dynamics in neurons, have emerged as critical players. Dysregulation of CLASP proteins is associated with NDDS, such as Alzheimer's, Parkinson's, and Huntington's diseases. Consequently, comprehending the role of CLASP proteins in NDDS holds promise for the development of innovative therapeutic interventions.

Objectives: The objectives of the research were to identify phytoconstituents in the hydroalcoholic extract of Uncaria tomentosa (HEUT), to evaluate its antioxidant potential through in vitro free radical scavenging assays and to explore its potential interaction with CLASP using in silico molecular docking studies.

Methods: HPLC and LC-MS techniques were used to identify and quantify phytochemicals in HEUT. The antioxidant potential was assessed through DPPH, ferric reducing antioxidant power (FRAP), nitric oxide (NO) and superoxide (SO) free radical scavenging methods. Interactions between conventional quinovic acid, chlorogenic acid, epicatechin, corynoxeine, rhynchophylline and syringic acid and CLASP were studied through in silico molecular docking using Auto Dock 4.2.

Results: The HEUT extract demonstrated the highest concentration of quinovic acid derivatives. HEUT exhibited strong free radical-scavenging activity with IC50 values of 0.113 μg/ml (DPPH) and 9.51 μM (FRAP). It also suppressed NO production by 47.1 ± 0.37% at 40 μg/ml and inhibited 77.3 ± 0.69% of SO generation. Additionally, molecular docking revealed the potential interaction of quinovic acid with CLASP for NDDS.

Conclusion: The strong antioxidant potential of HEUT and the interaction of quinovic acid with CLASP protein suggest a promising role in treating NDDS linked to CLASP protein dysregulation.

Background: Hyperlipidemia, a cardiovascular disease risk factor, is characterized by a rise in low-density lipoprotein (LDL), triglycerides and total cholesterol, and a decrease in high-density lipoprotein (HDL). Cholesteryl ester transfer protein (CETP) enables the transfer of cholesteryl ester from HDL to LDL and very low-density lipoprotein.

Objectives: CETP inhibition is a promising approach to prevent and treat cardiovascular diseases. By inhibiting lipid transport activity, it increases HDL levels and decreases LDL levels.

Materials and method: Herein, diaryl sulfonamides 6a-6g and 7a-7g were prepared, and the structure of these compounds was fully determined using different spectroscopic techniques.

Results: These compounds underwent biological evaluation in vitro and showed different inhibitory activities against CETP; 100% inhibitory activity was observed for compounds 7a-7g, while activities of compounds 6a-6g ranged up to 42.6% at 10 μM concentration. Pharmacophore mapping agreed with the bioassay results where the four aromatic ring compounds 7a-7g possessed higher fit values against Hypo4/8 and the shape-complemented Hypo4/8 in comparison to compounds 6a-6g.

Conclusion: Docking of the synthesized compounds using libdock and ligandfit engines revealed that compounds 7a-7g formed п-п stacking and hydrophobic interactions with the binding pocket, while compounds 6a-6g missed these hydrophobic interactions with amino acids Leu206, Phe265, and Phe263.

Background: Ginseng (Panax ginseng) is a herb of medicinal and nutritional importance. Ginseng has been used since ancient times for the treatment of numerous ailments as it has many therapeutic properties. Several phytoconstituents are present in Panax ginseng that possess a variety of beneficial pharmacological properties.

Objective: To explore the potential of phytoconstituents of Panax ginseng in the treatment of depression, a molecular modeling technique was utilized targeting monoamine oxidase-A (MAO-A).

Methods: A total of sixty-one phytoconstituents of ginseng were drawn with the help of ChemBioDraw Ultra 12.0 software and PDBs for MAO-A enzyme were retrieved from the RCSB PDB database. The prepared ligands were screened for MAO-A properties using the software Molegro Virtual Docker (MVD 2010.4.1.0). All the prepared ligands were evaluated for drug-likeliness properties using Swiss ADME.

Results: Among the docking studies of 60 Ginseng phytochemicals including one standard, 15 phytoconstituents with the highest dock score and better binding interactions were selected further for absorption, distribution, metabolism and excretion (ADME) studies. Stachyose (-227.287, 17 interactions), Raffinose (-222.157, 14 interactions), and Ginsenoside Rg1 (-216.593, 10 interactions) were found to possess better interactions as compared to Clorgyline taken as a standard drug.

Conclusion: Stachyose was found to be the most potent inhibitor of MAO-A enzyme under investigation and can be a potential lead molecule for the development of newer phytochemical-based treatment of depression.

Background: In China, Niuxi-Mugua formula (NMF) has been widely used to prevent and treat coronavirus disease 2019 (COVID-19). However, the mechanism of NMF for treating COVID-19 is not yet fully understood.

Objective: This study aimed to explore the potential mechanism of NMF for treating COVID- 19 by network pharmacology, computational biology, and surface plasmon resonance (SPR) verification.

Materials and methods: The NMF-compound-target network was constructed to screen the key compounds, and the Molecular Complex Detection (MCODE) tool was used to screen the preliminary key genes. The overlapped genes (OGEs) and the preliminary key genes were further analyzed by enrichment analysis. Then, the correlation analysis of immune signatures and the preliminary key genes was performed. Molecular docking and molecular dynamic (MD) simulation assays were applied to clarify the interactions between key compounds and key genes. Moreover, the SPR interaction experiment was used for further affinity kinetic verification.

Results: Lipid and atherosclerosis, TNF, IL-17, and NF-kappa B signaling pathways were the main pathways of NMF in the treatment of COVID-19. There was a positive correlation between almost the majority of immune signatures and all preliminary key genes. The key compounds and the key genes were screened out, and they were involved in the main pathways of NMF for treating COVID-19. Moreover, the binding affinities of most key compounds binding to key genes were good, and IL1B-Quercetin had the best binding stability. SPR analysis further demonstrated that IL1B-Quercetin showed good binding affinity.

Conclusion: Our findings provided theoretical grounds for NMF in the treatment of COVID-19.

Background: The coronavirus helicase NSP13 plays a critical role in its life cycle. The found NSP13 inhibitors have been tested only in vitro but they definitely have the potential to become antiviral drugs. Thus, the search for NSP13 inhibitors is of great importance.

Objectives: The goal of the present work was to develop a general approach to the design of ligands of coronaviral NSP13 helicase and to propose on its basis potential inhibitors.

Methods: The structure of the NSP13 protein was refined by molecular dynamics and the cavity, responsible for RNA binding, was chosen as the inhibitor binding site. The potential inhibitor structures were identified by molecular docking and their binding was verified by molecular dynamics simulation.

Results: A number of potential NSP13 inhibitors were identified and the binding modes and probable mechanism of action of potential inhibitors was clarified.

Conclusion: Using the molecular dynamics and molecular docking techniques, we have refined the structure of the coronavirus NSP13 helicase, a number of potential inhibitors, containing cage fragment were proposed and their probable mechanism of action was clarified. The proposed approach is also suitable for the design of ligands interacting with other viral helicases.

Background: The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases, and its pharmacological activities are being revisited in recent drug-repurposing research.

Objectives: Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation of its anti-cancer properties.

Materials and methods: A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters, namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g): solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained, and its GC-MS analysis is carried out. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract.

Results: The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with the R² value of 0.917 and adjusted R² of 0.865. Experimental results portray the significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals, while the constructed biological networks highlight its multi-targeted activity in colorectal cancer.

Conclusion: The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies.

Background: Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.

Aims: Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.

Objectives: In this study, a disease target identification algorithm based on protein recognition is proposed.

Materials and methods: In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.

Results: A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.

Conclusion: In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.