Current computer-aided drug design最新文献

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.

Background: Breast cancer is the prominent reason of death in women worldwide, and the cases are increasing day by day. There are many FDA-approved drugs for treating breast cancer. Due to drug resistance, and problems in selectivity, there is a need to develop more effective agents with few side effects. Indole derivatives have demonstrated significant pharmacological potential as anti-breast cancer agents. Further, chalcone derivatives incorporating heterocyclic scaffolds play a significant role in medicine. Indole-chalcone-based compounds offer the potential for improved biological activity and enhanced drug-like properties. It prompted us to explore the synthesis of Indole-Chalcone derivatives targeting estrogen receptor alpha (ER-α) to discover potent anti-breast cancer agents.

Objectives: To synthesize indole-chalcone derivatives and study their binding interactions for ER-α protein by molecular docking for breast cancer treatment.

Methods: In this study, indole-chalcone derivatives have been synthesized using conventional heating. With the help of Schrodinger software, molecular interaction as well as ADME (Adsorption, Distribution, Metabolism, and Excretion) studies of the compounds were conducted.

Results: Among all the synthesized compounds, four compounds (1, 2, 3, and 4) showed better docking scores (-10.24 kcal/mol, -10.15 kcal/mol, -9.40 kcal/mol, -9.29 kcal/mol, respectively) than the standard tamoxifen (-8.43 kcal/mol).

Conclusion: From In-silico studies, we can conclude that four compounds from the synthesized series fit into the active site of ER-α. ADME properties of synthesized derivatives were found in the acceptable range. In the future, these compounds can be further explored for biological activity.

Background: Resveratrol is a polyphenol that is found in plants and has been proposed to have a potential therapeutic effect through the activation of SIRT1, which is a crucial member of the mammalian NAD+ -dependent deacetylases. However, how its activity is enhanced toward specific substrates by resveratrol derivatives has not been studied. This study aimed to evaluate the types of interaction of resveratrol and its derivatives with SIRT1 as the target protein, as well as to find out the best ligand with the strangest interaction with SIRT1.

Materials and methods: In this study, we employed the extensive molecular docking analysis using AutoDock Vina to comparatively evaluate the interactions of resveratrol derivatives (22 molecules from the ZINC database) as ligands with SIRT1 (PDB ID: 5BTR) as a receptor. The ChemDraw and Chem3D tools were used to prepare 3D structures of all ligands and energetically minimize them by the MM2 force field.

Results: The molecular docking and visualizations showed that conformational change in resveratrol derivatives significantly influenced the parameter for docking results. Several types of interactions, including conventional hydrogen bonds, carbon-hydrogen bonds, Pi-donor hydrogen bonds, and Pi-Alkyl, were found via docking analysis of resveratrol derivatives and SIRT1 receptors. The possible activation effect of resveratrol 4'-(6-galloylglucoside) with ZINC ID: ZINC230079516 with higher binding energy score (-46.8608 kJ/mol) to the catalytic domain (CD) of SIRT1 was achieved at the maximum value for SIRT1, as compared to resveratrol and its other derivatives.

Conclusion: Finally, resveratrol 4'-(6-galloylglucoside), as a derivative for resveratrol, has stably interacted with the CD of SIRT1 and might be a potential effective activator for SIRT1.

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

Background: ICMT (isoprenylcysteine carboxyl methyltransferase) is an enzyme that plays a key role in the post-translational modification of the K-Ras protein. The carboxyl methylation of this protein by ICMT is important for its proper localization and function. Cysmethynil (2-[5-(3-methylphenyl)-l-octyl-lH-indolo-3-yl] acetamide) causes K-Ras mislocalization and interrupts pathways that control cancer cell growth and division through inhibition of ICMT, but its poor water solubility makes it difficult and impractical for clinical use. This indicates that relatively high amounts of cysmethynil would be required to achieve an effective dose, which could result in significant adverse effects in patients.

Objective: The general objective of this work was to find virtually new compounds that present high solubility in water and are similar to the pharmacological activity of cysmethynil.

Materials and methods: Pharmacophore modeling, pharmacophore-based virtual screening, prediction of ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), and water solubility were performed to recover a water-soluble molecule that shares the same chemical characteristics as cysmethynil using Discovery Studio v16.1.0 (DS16.1), SwissADME server, and pkCSM server.

Results: In this study, ten pharmacophore model hypotheses were generated by exploiting the characteristics of cysmethynil. The pharmacophore model validated by the set test method was used to screen the "Elite Library^®" and "Synergy Library" databases of Asinex. Only 1533 compounds corresponding to all the characteristics of the pharmacophore were retained. Then, the aqueous solubility in water at 25°C of these 1533 compounds was predicted by the Cheng and Merz model. Among these 1533 compounds, two had the optimal water solubility. Finally, the ADMET properties and Log S water solubility by three models (ESOL, Ali, and SILICOS-IT) of the two compounds and cysmethynil were compared, resulting in compound 2 as a potential inhibitor of ICMT.

Conclusion: According to the results obtained, the identified compound presented a high solubility in water and could be similar to the pharmacological activity of cysmethynil.

Background: Helicobacter pylori (HP) infection could lead to various gastrointestinal diseases. Urease is the most important virulence factor of HP. It protects the bacterium against gastric acid.

Objective: Therefore, we aimed to design urease inhibitors as drugs against HP infection.

Methods: The DrugBank-approved library was assigned with 3D conformations and the structure of the urease was prepared. Using a re-docking strategy, the proper settings were determined for docking by PyRx and GOLD software. Virtual screening was performed to select the best inhibitory drugs based on binding affinity, FitnessScore, and binding orientation to critical amino acids of the active site. The best inhibitory drug was then evaluated by IC₅₀ and the diameter of the zone of inhibition for bacterial growth.

Results: The structures of prepared drugs were screened against urease structure using the determined settings. Clodronic acid was determined to be the best-identified drug, due to higher PyRx binding energy, better GOLD FitnessScore, and interaction with critical amino acids of urease. In vitro results were also in line with the computational data. IC₅₀ values of Clodronic acid and Acetohydroxamic Acid (AHA) were 29.78 ± 1.13 and 47.29 ± 2.06 μg/ml, respectively. Diameters of the zones of inhibition were 18 and 15 mm for Clodronic acid and AHA, respectively.

Conclusion: Clodronic acid has better HP urease inhibition potential than AHA. Given its approved status, the development of a repurposed drug based on Clodronic acid would require less time and cost. Further, in vivo studies would unveil the efficacy of Clodronic acid as a urease inhibitor.