Current computer-aided drug design最新文献

The COVID-19 pandemic is raising a worldwide search for compounds that could act against the disease, mainly due to its mortality. With this objective, many researchers invested in the discovery and development of drugs of natural origin. To assist in this search, the potential of computational tools to reduce the time and cost of the entire process is known. Thus, this review aimed to identify how these tools have helped in the identification of natural products against SARS-CoV-2. For this purpose, a literature review was carried out with scientific articles with this proposal where it was possible to observe that different classes of primary and, mainly, secondary metabolites were evaluated against different molecular targets, mostly being enzymes and spike, using computational techniques, with emphasis on the use of molecular docking. However, it is noted that in silico evaluations still have much to contribute to the identification of an anti- SARS-CoV-2 substance, due to the vast chemical diversity of natural products, identification and use of different molecular targets and computational advancement.

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) poses an enormous challenge to human health and economy at a global level. According to WHO's latest data, till now, there have been a total of 641,435,884 confirmed cases of COVID-19, and the associated deaths are 6,621,060. Though few vaccinations have been approved for emergency usage, antiviral medications for long-term therapeutics are still being sought. The current research seeks to identify the inhibitory effect of iminosugars, particularly 1-deoxynojirmycin (IDNJ) series, against SARS-CoV-2 main protease (SARS-CoV2-Mpro) using an inhibitor optimization approach for 1DNJ series.

Aim: The aim of this study was to investigate the inhibitory effect of iminosugars, specifically 1-deoxynojirmycin (1-DNJ) derivatives, on SARS-CoV-2 main protease (Mpro) as it plays a vital role in viral propagation and transcription and is shaped like a heart.

Objective: The main objective of this study was to find the possibility of 1-DNJ derivatives being potent inhibitors against SARS CoV2 Mpro. This study was focused on finding the most probable conformation in which DNJ derivatives could bind to Mpro. Another objective was to obtain molecular-level details by getting insights into stable interactions formed between the ligand and receptor.

Method: In silico molecular mechanics (MM) based techniques were employed to identify the best-docked inhibitors using molecular docking, and complexes that showed stable interactions were further subjected to 200 ns of molecular dynamics (MD) simulations to check the stability of ligand into the binding pocket of SARS-CoV2-Mpro. The inhibitors that formed stable complexes were further tested for their ADME properties in order to check the pharmacokinetic parameters as well as their therapeutic importance.

Result: Docking was performed on 29 compounds from two different series against SARS-CoV-2 main protease, Mpro (PDB ID: 6LZE). Twelve compounds were found to have high docking scores and better interactions with the active site of Mpro, as compared to the co-crystallized ligand. Furthermore, the three highest-scoring docked compounds (17a, 7, and 8) depicted strong and stable complex formation, throughout the 200 ns molecular dynamics simulation, by analyzing the binding energy (MM/GBSA). The molecules were discovered to form stable interactions with conserved active-site residues, which play an important role in demonstrating activity in structure-based drug design. The ADMET analysis was performed using Qikprop, and the proposed stable derivatives passed all of the needed drug discovery standards, potentially inhibiting the Mpro of SARS-CoV-2.

Conclusion: The present findings confer opportunities for compounds 17a, 7, and 8 that could be developed as new therapeutic agents against COVID-19. These compounds are

Background: Rhizoma drynariae, a classic prescription in traditional Chinese medicine, has long been used for the treatment of osteonecrosis of the femoral head (ONFH), but its potential targets and molecular mechanisms remain to be further explored.

Objective: This study aims to explore the mechanism of Rhizoma drynariae in ONFH treatment via network pharmacology and in vitro experiments.

Methods: Targets of Rhizoma drynariae and ONFH were predicted using relevant databases, and intersection analysis was conducted to screen for shared targets. A PPI network of the shared targets was built using STRING to identify the key targets. Functional enrichment analyses of Gene Ontology and KEGG pathway data were carried out using R software. The compound-target-pathway network was constructed for Rhizoma Drynariae in the treatment with ONFH using Cytoscape 3.9.0. Cell proliferation was assessed using CCK8 and apoptosis was detected using (Propidium Iodide) PI staining and western blotting.

Results: This study depicts the interrelationship of the bioactive compounds of Rhizoma drynariae with ONFH-associated signaling pathways and target receptors and is a potential reagent for ONFH treatment.

Conclusion: Based on a network pharmacology analysis and in vitro experiment, we predicted and validated the active compounds and potential targets of Rhizoma drynariae, provide valuable evidence of Rhizoma Drynariae in future ONFH treatment.

Aim: To provide new methods and ideas for the clinical application of integrated traditional Chinese and Western medicine in the treatment of esophageal cancer.

Background: Traditional Chinese medicine compound Kushen injection (CKI) has been widely used in the clinic with adjuvant radiotherapy and chemotherapy. However, the mechanism of action of CKI as adjuvant therapy for esophageal cancer has not yet been described.

Methods: This study is based on network pharmacology, data mining, and molecular docking technology to explore the mechanism of action of CKI in the treatment of esophageal cancer. We obtained the effective ingredients and targets of CKI from the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) and esophageal cancer-related genes from the Online Mendelian Inheritance in Man (OMIM) and GeneCards databases.

Results: CKI mainly contains 58 active components. Among them, the top 5 active ingredients are quercetin, luteolin, naringenin, formononetin, and beta-sitostero. The target protein of the active ingredient was matched with the genes associated with esophageal cancer. The active ingredients targeted 187 esophageal cancer target proteins, including AKT1, MAPK1, MAPK3, TP53, HSP90AA1, and other proteins. Then, we enriched and analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and used AutoDockVina to dock the core targets and compounds. Finally, PyMOL and Ligplot were used for data visualization.

Conclusion: This study provides a new method and ideas for the clinical application of integrated traditional Chinese and Western medicine in the treatment of esophageal cancer.