Current computer-aided drug design最新文献

A malignant tumor is a frequent and common disease that severely threatens human health. Many mechanisms, such as cell signaling pathway, anti-apoptosis mechanism, cell stemness, metabolism, and cell phenotype, have been studied to explain the reasons for chemotherapy, radioresistance, and tumor recurrences in antitumor treatment. Cancer stem cells (CSCs) are important tumor cell subclasses that can potentially organize and regulate stem cell properties. Growing evidence suggests that CSCs can initiate tumors and constitute a significant factor in metastasis, recurrence, and treatment resistance. The inability to completely target and remove CSCs is a considerable obstacle in tumor treatment. Therefore, drugs and therapeutic strategies that can effectively intervene with CSCs are essential for the treatment of different tumor types. However, the current strategies and efficacy of targeted elimination of CSCs are very limited. Oxidative stress has been recognized to play a crucial role in cancer pathophysiology. Moreover, reactive oxygen species (ROS) production and imbalance of the built-in cellular antioxidant defense system are hallmarks of tumor and cancer etiology. The current paper will focus on the regulation and mechanism behind oxidative stress in tumors and cancer stem cells and its tumor therapy applications. Additionally, the article discusses the role of CSCs in causing tumor treatment resistance and recurrence based on a redox perspective. The study also emphasizes that targeted modulation of oxidative stress in CSCs has great potential in tumor therapy, providing novel prospects for tumor therapy.

Objective: The NLRP3 inflammasome mediates a range of inflammatory responses that are associated with an increasing number of pathological mechanisms. Over-activation of NLRP3 can exacerbate many diseases. However, NLRP3 antagonists have significant therapeutic potential. Moreover, NLRP3 plays an important role in limiting the growth and spread of some tumors, and NLRP3 agonists also have clinical value. MCC950 and BMS986299 are an antagonist and agonist of NLRP3, respectively. In light of the important clinical applications of NLRP3, especially for NLRP3 inhibitors, a computational method was used to investigate the interaction modes of MCC950 and BMS986299 with NLRP3 in order to design and develop more potent NLRP3 regulators.

Methods: In this study, the conformational behaviors of NLRP3 bound to the antagonist MCC950 in an inactive state and the agonist BMS986299 in an active state were investigated using 200 ns equilibrium all-atom molecular dynamics (MD) simulations, and then the analyses of the MD trajectories (RMSD, Rg, RMSF, SASA, PCA, and DCCM) were carried out to explore the mechanism of the antagonist and agonist on NLRP3 in the two different states.

Results: The RMSD, RMSF, Rg, SASA, and PCA analyses indicated that NLRP3 was more dispersive and less energetically stable in the active state than in the inactive state and that MCC950 significantly reduced the fluctuations of the interactive residues while BMS986299 did not. The antagonist MCC950 interacted with residues mainly in the NBD, HD1, WHD, and HD2 domains of NLRP3, whereas the agonist BMS986299 mainly in the NBD and WHD of NLRP3. Additionally, both compounds did not interact with residues located in the FISNA domain. The conformation of the FISNA domain appeared to change significantly when NLRP3 was translated from an inactive state to an active state.

Conclusion: The antagonist may interact with residues mainly in the NBD, HD1, WHD, and HD2 domains, and the agonist may interact in the NBD and WHD domains. Our study provided new insights into the development of NLRP3 regulators.

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing globally, impacting individuals in Western nations and rapid growing in Asian countries due to sedentary lifestyles; thus, NAFLD has emerged as a significant worldwide health concern. Presently, lifestyle changes represent the primary approach to managing NAFLD.

Methods: This research aimed to identify the potential drug targets for treating NAFLD through comprehensive in silico computational analysis. These include the prediction of the three-dimensional structure of the protein, the prediction of inhibitors by PubChem and ZINC, molecular docking by Autodcok, pharmacophore modeling, molecular dynamics simulation by the OPLS_2005 force field, and the orthorhombic box solvent model Intermolecular Interaction Potential 3 Points Transferable to the selected compound. The toxicity of the lead compounds was analyzed through AdmetSAR software.

Results: The protein associated with the PNPLA3 gene, whose overall three-dimensional structure was 95% accurate, were retrieved following inhibitor selection via PubChem and ZINC. Among the selected inhibitors and docked compounds with ID 10033935 (ellagitannin) showed a minimum E-Score of -17.266. In docking and pharmacophore modeling the compound ellagitannin shows promise as a potential drug candidate. Moreover, the molecular dynamics and structural stability of the protein-ligand complex were evaluated with several metrics such as as root mean square fluctuation and root mean square deviation and resulted in the stability not only of PNPLA3-10033935 (ellagitannin) but also of compound PNPLA3-71448940 and PNPLA3-5748394 complexed proteins at 400 ns with very slight variation.

Conclusion: Overall, ellagitannin was identified as the best druggable target with the best therapeutics profile. The findings of our study can pave the way for the development of a new drug against NALFD.

Introduction: Astrocytoma is the most common glioma, accounting for about 65% of glioblastoma. Its malignant transformation is also one of the important causes of patient mortality, making it the most prevalent and difficult to treat in primary brain tumours. However, little is known about the underlying mechanisms of this transformation.

Methods: In this study, we established a ceRNA network to screen out the potential regulatory pathways involved in the malignant transformation of IDH-mutant astrocytomas. Firstly, the Chinese Glioma Genome Atlas (CGGA) was employed to compare the expression levels of the differential expressed genes (DEGs) in astrocytomas. Then, the ceRNA-regulated network was constructed based on the interaction of lncRNA-miRNA-mRNA. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore the main functions of the differentially expressed genes. COX regression analysis and log-rank test were combined to screen the ceRNA network further. In addition, quantitative real-time PCR (qRT-PCR) was conducted to identify the potential regulatory mechanisms of malignant transformation in IDH-mutant astrocytoma.

Results: A ceRNA network with 34 lncRNAs, 29 miRNAs, and 71 mRNAs. GO and KEGG analyses results suggested that DEGs were associated with tumor-associated molecular functions and pathways. In addition, we screened two ceRNA regulatory networks using Cox regression analysis and log-rank test. QRT-PCR assay identified the NAA11/hsa-miR-142-3p/GS1-39E22.2 regulatory axis of the ceRNA network to be associated with the malignant transformation of IDH-mutant astrocytoma.

Conclusion: The discovery of this mechanism deepens our understanding of the molecular mechanisms of malignant transformation in astrocytomas and provides new perspectives for exploring glioma progression and targeted therapies.

Introduction: The prevalence of breast cancer presents a substantial global health concern, underscoring the ongoing need for the development of inventive therapeutic remedies.

Methods: In this investigation, an array of novel indazole-pyridine hybrids (5a-h) have been designed and synthesized to assess their potential as candidates for treating breast cancer. Subsequently, we have conducted biological evaluations to determine their cytotoxic effects on the human MCF-7 breast cancer cell line. Furthermore, in silico analysis was conducted to estimate the inhibition potential of the compounds against TrkA (Tropomyosin receptor kinase A), a specific molecular target associated with breast cancer, through molecular docking. In silico physicochemical and pharmacokinetic predictions were made to assess the compounds' drug-like properties.

Results: Compound 5a emerged as the most active compound among the others with GI50 < 10 μg/ml. Besides, compound 5a showed high binding energy (BE -10.7 kcal/mol) against TrkA and was stabilized within the TrkA binding pocket through hydrophobic, H-bonding, and van der Waals interactions. In silico physicochemical and pharmacokinetic prediction studies indicated that compound 5a obeyed both Lipinski's and Veber's rule and displayed a versatile pharmacokinetic profile, implying compound 5a to appear as a viable candidate and that it could be further refined to develop therapeutic agents for potentially treating breast cancer.

Conclusion: This study offers a promising direction for the advancement of innovative breast cancer treatments, highlighting the effectiveness of indazole-pyridine hybrids as potential anticancer agents. Further optimization and preclinical development are necessary to advance these compounds to clinical trials.

Background: Cinnamic acid (Cinn) is a phenolic acid of Cinnamomum cassia (L.) J. Presl. that can ameliorate diabetic nephropathy (DN). However, comprehensive therapeutic targets and underlying mechanisms for Cinn against DN are limited.

Objective: In this study, a network pharmacology approach and in vivo experiments were adopted to predict the pharmacological effects and mechanisms of Cinn in DN therapy.

Methods: The nephroprotective effect of Cinn on DN was investigated by a streptozotocininduced diabetes mellitus (DM) mouse model. The protein-protein interaction network of Cinn against DN was established by a network pharmacology approach. The core targets were then identified and subjected to molecular docking with Cinn.

Results: Cinn treatment effectively restored body weight, ameliorated hyperglycemia, and reduced kidney dysfunction markers in DM mice, also demonstrating a reduction in tissue injury. Network pharmacology analysis identified 298 DN-Cinn co-target genes involved in various biological processes and pathways. Seventeen core targets were identified, eight of which showed significant differential expression in the DN and healthy control groups. Molecular docking analysis revealed a strong interaction between Cinn and PTEN. Cinn treatment downregulated the PTEN protein expression in DM mice.

Conclusion: This study revealed the multi-target and multi-pathway characteristics of Cinn against DN. Cinn improved renal pathological damage of DN, which was related to the downregulation of PTEN.

Background: Sepsis-related acute respiratory distress syndrome (ARDS) is a fatal disease without effective therapy. Kaempferol is a flavonoid compound extracted from natural plant products; it exerts numerous pharmacological effects. Kaempferol attenuates sepsis-related ARDS; however, the underlying protective mechanism has not been elucidated completely.

Objectives: This study aimed to use network pharmacology and experimental verification to investigate the mechanisms by which kaempferol attenuates sepsis-related ARDS.

Methods: We screened the targets of kaempferol by PharMapper, Swiss Target Prediction, and CTD database. We identified the targets of sepsis-related ARDS by GeneCards, DisGeNet, OMIM, and TTD. The Weishengxin platform was used to map the targets of both kaempferol and sepsis-related ARDS. We created a Venn diagram to identify the intersection targets. We constructed the "component-intersection targets-disease" network diagram using Cytoscape 3.9.1 software. The intersection targets were imported into the STRING database for developing the protein-protein interaction network. Metascape was used for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We selected the leading 20 KEGG pathways to establish the KEGG relationship network. Finally, we performed experimental verification to confirm our prediction results.

Results: Through database screening, we obtained 502, 360, and 78 kaempferol targets, disease targets of sepsis-related ARDS, and intersection targets, respectively. The core targets consisted of tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, albumin (ALB), IL-1β, and AKT serine/ threonine kinase (AKT)1. GO enrichment analysis identified 426 items, which were principally involved in response to lipopolysaccharide, regulation of inflammatory response, inflammatory response, positive regulation of cell migration, positive regulation of cell adhesion, positive regulation of protein phosphorylation, response to hormone, regulation of reactive oxygen species (ROS) metabolic process, negative regulation of apoptotic signaling pathway, and response to decreased oxygen levels. KEGG enrichment analysis identified 151 pathways. After eliminating the disease and generalized pathways, we obtained the hypoxia-inducible factor 1 (HIF-1), nuclear factor κB (NF-κB), and phosphoinositide 3-kinase (PI3K)-Akt signaling pathways. Our experimental verification confirmed that kaempferol blocked the HIF-1, NFκκB, and PI3K-Akt signaling pathways, diminished TNF-α, IL-1β, and IL-6 expressions, suppressed ROS production, and inhibited apoptosis in lipopolysaccharide (LPS)-induced murine alveolar macrophage (MH-S) cells.

Conclusion: Kaempferol can reduce inflammatory response, ROS production, and cell apoptosis by acting on the HIF-1, NF-κB, and PI3K-Akt signaling pathways, thereby all

Traditional molecular de novo generation methods, such as evolutionary algorithms, generate new molecules mainly by linking existing atomic building blocks. The challenging issues in these methods include difficulty in synthesis, failure to achieve desired properties, and structural optimization requirements. Advances in deep learning offer new ideas for rational and robust de novo drug design. Deep learning, a branch of machine learning, is more efficient than traditional methods for processing problems, such as speech, image, and translation. This study provides a comprehensive overview of the current state of research in de novo drug design based on deep learning and identifies key areas for further development. Deep learning-based de novo drug design is pivotal in four key dimensions. Molecular databases form the basis for model training, while effective molecular representations impact model performance. Common DL models (GANs, RNNs, VAEs, CNNs, DMs) generate drug molecules with desired properties. The evaluation metrics guide research directions by determining the quality and applicability of generated molecules. This abstract highlights the foundational aspects of DL-based de novo drug design, offering a concise overview of its multifaceted contributions. Consequently, deep learning in de novo molecule generation has attracted more attention from academics and industry. As a result, many deep learning-based de novo molecule generation types have been actively proposed.

Background: Non-alcoholic Fatty Liver Disease (NAFLD) has become a significant health and economic burden globally. Yinchenhao decoction (YCHD) is a traditional Chinese medicine formula that has been validated to exert therapeutic effects on NAFLD.

Objective: The current study aimed to explore the pharmacological mechanisms of YCHD on NAFLD and further identify the potential active compounds acting on the main targets.

Methods: Compounds in YCHD were screened and collected from TCMSP and published studies, and their corresponding targets were obtained from the Swisstargetprediction and SEA databases. NAFLD-related targets were searched in the GeneCards and DisGeNet databases. The "compound-intersection target" network was constructed to recognize the key compounds. Moreover, a PPI network was constructed to identify potential targets. GO and KEGG analyses were performed to enrich the functional information of the intersection targets. Then, molecular docking was used to identify the most promising compounds and targets. Finally, molecular dynamics (MD) simulations were performed to verify the binding affinity of the most potential compounds with the key targets.

Results: A total of 53 compounds and 556 corresponding drug targets were collected. Moreover, 2684 NAFLD-related targets were obtained, and 201 intersection targets were identified. Biological processes, including the apoptotic process, inflammatory response, xenobiotic metabolic process, and regulation of MAP kinase activity, were closely related to the treatment of NAFLD. Metabolic pathways, non-alcoholic fatty liver disease, the MAPK signaling pathway, and the PI3K-Akt signaling pathway were found to be the key pathways. Molecular docking showed that quercetin and isorhamnetin were the potential active compounds, while AKT1, IL1B, and PPARG were the most promising targets. MD simulations further verified that the binding of PPARG-isorhamnetin (-35.96 ± 1.64 kcal/mol) and AKT1-quercetin (-31.47 ± 1.49 kcal/mol) was due to their lowest binding free energy.

Conclusion: This study demonstrated that YCHD exerts therapeutic effects for the treatment of NAFLD through multiple targets and pathways, providing a theoretical basis for further pharmacological research on the potential mechanisms of YCHD in NAFLD.

Background: Astragaloside IV (AS-IV) has been shown to have a curative effect on non-small cell lung cancer (NSCLC). This study aimed to elucidate the role of AS-IV in NSCLC cell anlotinib resistance (AR).

Methods: The NSCLC/AR cells, resistant to anlotinib, have been produced. The role of AS-IV in the AR of NSCLC cells about the miR-181a-3p/unfolded protein response (UPR)- endoplasmic reticulum associated degradation (ERAD) pathway was then discussed by treating the cells with anlotinib or AS-IV, or by manipulating them with inhibitors or mimics of miR- 181a-3p, HRD1 or Derlin-1 overexpression plasmids.

Results: We found that AS-IV could suppress the AR of NSCLC cells. In addition, miR-181a- 3p was elevated in NSCLC/AR cells. Functionally, AS-IV limited the AR of NSCLC cells by reducing miR-181a-3p. Further, activation of the UPR-ERAD pathway was correlated with AR in NSCLC cells. Increased sensitivity of NSCLC cells to anlotinib caused by miR-181a-3p inhibitor could be reversed by overexpression of HRD1 or Derlin-1.

Conclusion: This research revealed a promising NSCLC/AR treatment approach by showing that AS-IV exposed NSCLC cells to anlotinib by inhibiting the miR-181a-3p/UPR-ERAD axis.