Current computer-aided drug design最新文献

Introduction: Skin cancer is the most common type of cancer caused by the uncontrolled growth of abnormal cells in the epidermis and the outermost skin layer.

Aim: This study aimed to study the anti-skin cancer potential of [6]-Gingerol and 21 related structural analogs using in vitro and in silico studies.

Methods: The ethanolic crude extract of the selected plant was subjected to phytochemical and GC-MS analysis to confirm the presence of the [6]-gingerol. The anticancer activity of the extract was evaluated by MTT (3-[4, 5-dimethylthiazol-2-y]-2, 5-diphenyl tetrazolium bromide) assay using the A431 human skin adenocarcinoma cell line.

Results: The GC-MS analysis confirmed the presence of [6]-Gingerol compound, and its promising cytotoxicity IC₅₀ was found at 81.46 ug/ml in the MTT assay. Furthermore, the in silico studies used [6]-Gingerol and 21 structural analogs collected from the PubChem database to investigate the anticancer potential and drug-likeliness properties. Skin cancer protein, DDX3X, was selected as a target that regulates all stages of RNA metabolism. It was docked with 22 compounds, including [6]-Gingerol and 21 structural analogs. The potent lead molecule was selected based on the lowest binding energy value.

Conclusion: Thus, the [6]-Gingerol and its structure analogs could be used as lead molecules against skin cancer and future drug development process.

Background: Rheumatoid Arthritis (RA) is a chronic autoimmune disease that can lead to joint pain and disability, and seriously impact patients' quality of life. Strychni Semen combined with Atractylodes Macrocephala koidz (SA) have pronounced curative effect on RA, and there is no poisoning of Strychni Semen (SS). However, its pharmacological mechanisms are still unclear.

Objective: In this study, we aimed to investigate the pharmacological mechanisms of Strychni Semen combined with Atractylodes Macrocephala Koidz (SA) for the treatment of RA.

Methods: We used network pharmacology to screen the active components of SA and predict the targets and pathways involved. Results originating from network pharmacology were then verified by animal experiments.

Results: Network pharmacology identified 81 active ingredients and 141 targets of SA; 2640 disease- related genes were also identified. The core targets of SA for the treatment of RA included ALB, IL-6, TNF and IL-1β. A total of 354 gene ontology terms were identified by Gene ontology (GO) enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results showed that SA was closely associated with TNF signaling pathways in the treatment of RA. Furthermore, according to the predicted results of network pharmacology, we established a rat model of Adjuvant Arthritis (AA) for in vivo experiments. Analysis showed that each treatment group led to an improvement in paw swelling, immune organ coefficient and synovial tissue morphology in AA rats to different degrees, inhibit the expression levels of IL-1β, TNF-α and IL-6, upregulated the levels of Fas, Bax and Caspase 3, down-regulated the expression levels of Fas-L, Bcl-2 and p53.

Conclusion: SA has an anti-RA effect, the mechanism underlying the therapeutic action of SA in AA rats was related to the regulation of apoptosis signaling pathways.

Background: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis.

Objective: The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA).

Methods: Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis.

Results: Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients.

Conclusion: We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

Background: Prostate cancer is one of the most prevalent cancers in men, leading to the second most common cause of death in men. Despite the availability of multiple treatments, the prevalence of prostate cancer remains high. Steroidal antagonists are associated with poor bioavailability and side effects, while non-steroidal antagonists show serious side effects, such as gynecomastia. Therefore, there is a need for a potential candidate for the treatment of prostate cancer with better bioavailability, good therapeutic effects, and minimal side effects.

Objective: This current research work focused on identifying a novel non-steroidal androgen receptor antagonist through computational tools, such as docking and in silico ADMET analysis.

Methods: Molecules were designed based on a literature survey, followed by molecular docking of all designed compounds and ADMET analysis of the hit compounds.

Results: A library of 600 non-steroidal derivatives (cis and trans) was designed, and molecular docking was performed in the active site of the androgen receptor (PDBID: 1Z95) using Auto- Dock Vina 1.5.6. Docking studies resulted in 15 potent hits, which were then subjected to ADME analysis using SwissADME. ADME analysis predicted three compounds (SK-79, SK-109, and SK-169) with the best ADME profile and better bioavailability. Toxicity studies using Protox-II were performed on the three best compounds (SK-79, SK-109, and SK-169), which predicted ideal toxicity for these lead compounds.

Conclusion: This research work will provide ample opportunities to explore medicinal and computational research areas. It will facilitate the development of novel androgen receptor antagonists in future experimental studies.

Background: Cancer is one of the most dangerous illnesses to the human body due to its severity and progressive nature. Kaposi's Sarcoma (KS) tumor can appear as painless purple spots on the legs, foot, or face. This cancer develops in the lining of lymph arteries and blood vessels. Along with the enlargement of lymph nodes, the vaginal region and the mouth portion are the additional target areas of KS. DNA-binding proteins known as Sox proteins are found in all mammals and belong to the HMG box superfamily. They controlled a wide range of developmental procedures, such as the formation of the germ layer, the growth of organs, and the selection of the cell type. Human developmental abnormalities and congenital illnesses are frequently caused by the deletion or mutation of the Sox protein.

Aim: The purpose of this study is to determine the promising Kaposi's sarcoma inhibitors through computational studies.

Objective: In this present study computational approaches were used to evaluate the anti- carcinogenic efficacy against Kaposi's sarcoma.

Methods: Ligand-based pharmacophore screening was performed utilising four different chemical libraries (Asinex, Chembridge, Specs, and NCI Natural products (NSC)) depending on the top hypothesis. The top hits were examined using molecular docking, absorption, distribution, metabolism and excretion. Highest occupied molecular orbital and lowest unoccupied molecular orbital were analysed to determine the lead compounds' biological and pharmacological efficacy. The results of the study indicated that the leading candidates were possible SOX protein inhibitors.

Results: A pharmacophore model to inhibit the production of SOX protein in Kaposi Sarcoma was generated in this computational experiment using a set of 19 Chitosan compounds.

Conclusion: The results revealed that the top hits responded to all of the pharmacological druglikening criteria and had the best interaction residues, fitness scores, and docking scores. The resulting leads might be potential Kaposi's Sarcoma alternative treatments.

Background: Esters of quinoxaline-7-carboxylate 1,4-di-N-oxide (7-carboxylate QdNOs) derivatives are compounds that inhibit the growth of Entamoeba histolytica, the causative agent of amebiasis. Although these compounds cause changes in the redistribution of glycogen deposits within the parasite, it is unknown whether these compounds interact with enzymes of the glycolytic pathway.

Objective: The aim of this study was to test the binding affinity of these compounds to pyrophosphate- dependent phosphofructokinase (PPi-PFK), triosephosphate isomerase (TIM), and pyruvate phosphate dikinase (PPDK) from E. histolytica as a possible mechanism of action.

Methods: The molecular docking study of the 7-carboxylate QdNOs derivatives and the proteins was performed using AutoDock/Vina software. Molecular dynamics simulation was performed for 100 ns.

Results: Among all the selected compounds, T-072 exhibited the best binding affinity to EhPPi- PFK and EhTIM proteins, while T-006 interacted best with EhPPDK. ADMET analysis revealed that T-072 was non-toxic, while T-006 could become harmful to the host. In addition, molecular dynamics showed that T-072 has stable interaction with EhPPi-PFK and EhTIM.

Conclusion: Including all aspects, these data indicated that these compounds might inhibit the activity of key enzymes in energy metabolism leading to parasite death. Furthermore, these compounds may be a good starting point for the future development of new potent antiamebic agents.

Introduction: Integration of viral DNA into the host cell genome, carried out by the HTLV-1 integrase enzyme, is a crucial step in the Human T-lymphotropic Virus type I (HTLV-1) life cycle. Thus, HTLV-1 integrase is considered an attractive therapeutic target; however, no clinically effective inhibitors are available to treat HTLV-1 infection.

Objective: The main objective was to identify potential drug-like compounds capable of effectively inhibiting HTLV-1 integrase activity.

Methods: In this study, a model of HTLV-1 integrase structure and three integrase inhibitors (dolutegravir, raltegravir, and elvitegravir as scaffolds) were used for designing new inhibitors. Designed molecules were used as templates for virtual screening to retrieve new inhibitors from PubChem, ZINC15, and ChEMBL databases. Drug-likeness and docked energy of the molecules were investigated using the SWISS-ADME portal and GOLD software. Stability and binding energy of the complexes were further investigated using molecular dynamic (MD) simulation.

Results: Four novel potential inhibitors were developed using a structure-based design protocol and three compounds from virtual screening. They formed hydrogen bonding interactions with critical residues Asp69, Asp12, Tyr96, Tyr143, Gln146, Ile13, and Glu105. In addition, π stacking, halogen, and hydrogen bond interactions were seen between compounds (especially halogenated benzyl moieties) and viral DNA similar to those seen in parent molecules. MD simulation confirmed higher stability of the receptor-ligand complex than the ligand-free enzyme.

Conclusion: Combing structure-based design and virtual screening resulted in identifying three drug-like molecules (PubChem CID_138739497, _70381610, and _140084032) that are suggested as lead compounds for developing effective drugs targeting HTLV-1 integrase enzyme.

Background: Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation.

Methods: We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration.

Results: We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells.

Conclusion: Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Background: SARS-CoV-2 is a life-threatening virus in the world. Scientific evidence indicates that this pathogen will emerge again in the future. Although the current vaccines have a pivotal role in the control of this pathogen, the emergence of new variants has a negative impact on their effectiveness.

Objectives: Therefore, it is urgent to consider the protective and safe vaccine against all subcoronavirus species and variants based on the conserved region of the virus. Multi-epitope peptide vaccine (MEV), comprised of immune-dominant epitopes, is designed by immunoinformatic tools and it is a promising strategy against infectious diseases.

Methods: Spike glycoprotein and nucleocapsid proteins from all coronavirus species and variants were aligned and the conserved region was selected. Antigenicity, toxicity, and allergenicity of epitopes were checked by a proper server. To robust the immunity of the multi-epitope vaccine, cholera toxin b (CTB) and three HTL epitopes of tetanus toxin fragment C (TTFrC) were linked at the N-terminal and C-terminal of the construct, respectively. Selected epitopes with MHC molecules and the designed vaccines with Toll-like receptors (TLR-2 and TLR-4) were docked and analyzed. The immunological and physicochemical properties of the designed vaccine were evaluated. The immune responses to the designed vaccine were simulated. Furthermore, molecular dynamic simulations were performed to study the stability and interaction of the MEV-TLRs complexes during simulation time by NAMD (Nanoscale molecular dynamic) software. Finally, the codon of the designed vaccine was optimized according to Saccharomyces boulardii.

Results: The conserved regions of spike glycoprotein and nucleocapsid protein were gathered. Then, safe and antigenic epitopes were selected. The population coverage of the designed vaccine was 74.83%. The instability index indicated that the designed multi-epitope was stable (38.61). The binding affinity of the designed vaccine to TLR2 and TLR4 was -11.4 and -11.1, respectively. The designed vaccine could induce humoral and cellular immunity.

Conclusion: In silico analysis showed that the designed vaccine is a protective multi-epitope vaccine against SARS-CoV-2 variants.