Current computer-aided drug design最新文献

Background: Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

Objective: This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP.

Methods: OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells.

Results: A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast.

Conclusion: CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

Objective: The study aimed to explore the crucial genes involved in cancer-related biological processes, including EMT, autophagy, apoptosis, anoikis, and metastasis. It also sought to identify common genes among the pathways linked to these biological processes, determine the level of Bcl-2 expression in various types of cancers, and find a potent inhibitor of Bcl-2 among natural compounds.

Methods: Common genes involved in the pathways related to EMT, autophagy, apoptosis, anoikis, and metastasis were explored, and the level of the most frequently overexpressed gene that was Bcl-2, in various types of cancers was analyzed by gene expression analysis. A set of 102 natural compounds was sorted according to their docking scores using molecular docking and filtering. The top-ranked molecule was chosen for additional molecular dynamics (MD) simulation for 100 ns. Differential gene expression analysis was performed for Dioscin using GEO2R.

Results: The study identified four common genes, Bcl-2, Bax, BIRC3, and CHUK, among the pathways linked to EMT, autophagy, apoptosis, anoikis, and metastasis. Bcl-2 was highly overexpressed in many cancers, including Acute Myeloid Leukemia, Diffuse large B cell lymphoma, and Thymoma. The Dioscin structure in the Bcl-2 binding site received the highest docking score and the most relevant interactions. Dioscin's determined binding free energy by MM/GBSA was -52.21 kcal/mol, while the same calculated by MM/PBSA was -9.18 kcal/mol. A p-value of less than 0.05 was used to determine the statistical significance of the analysis performed using GEO2R. It was observed that Dioscin downregulates Bcl-2, BIRC3, and CHUK and upregulates the pro-apoptotic protein Bax.

Conclusion: The study concluded that Dioscin has the potential to act as a protein inhibitor, with a noteworthy value of binding free energy and relevant interactions with the Bcl-2 binding site. Dioscin might be a good alternative for targeting multiple cancer pathways through a single target.

Introduction: Transcription factors are vital biological components that control gene expression, and their primary biological function is to recognize DNA sequences. As related research continues, it was found that the specificity of DNA-protein binding has a significant role in gene expression, regulation, and especially gene therapy. Convolutional Neural Networks (CNNs) have become increasingly popular for predicting DNa-protein-specific binding sites, but their accuracy in prediction needs to be improved.

Methods: We proposed a framework for combining multi-Instance Learning (MIL) and a hybrid neural network named WSHNN. First, we utilized sliding windows to split the DNA sequences into multiple overlapping instances, each instance containing multiple bags. Then, the instances were encoded using a K-mer encoding. Afterward, the scores of all instances in the same bag were calculated separately by a hybrid neural network.

Results: Finally, a fully connected network was utilized as the final prediction for that bag. The framework could achieve the performances of 90.73% in Pre, 82.77% in Recall, 87.17% in Acc, 0.8657 in F1-score, and 0.7462 in MCC, respectively. In addition, we discussed the performance of K-mer encoding. Compared with other art-of-the-state efforts, the model has better performance with sequence information.

Conclusion: From the experimental results, it can be concluded that Bi-directional Long-ShortTerm Memory (Bi-LSTM) can better capture the long-sequence relationships between DNA sequences (the code and data can be visited at https://github.com/baowz12345/Weak_ Super_Network).

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: Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency.

Methods: The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study.

Results: The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study.

Conclusion: This in-silico modelling study has identified the natural potential lead (s) from Allium sativum. Specifically, the ajoene with the best in-silico features can be considered for further in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.

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.

Objective: 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, NF-κB, 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 alle

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: The Computer-Aided Drug Discovery (CADD) approach was used to develop a few Epidermal Growth Factor Receptor (EGFR) kinase inhibitors. EGFR kinase expression is highly associated with genomic instability, higher proliferation, lower hormone receptor levels, and HER2 over-expression. It is more common in breast cancer. Thus, EGFR Kinase is one of the main targets in discovering new cancer medicine.

Objective: To computationally validate some amides substituted β-amino enones as EGFR inhibitors and to carry out associated in vitro anticancer agents.

Methods: We used tools such as molecular docking, MD simulations, DFT calculations, and ADMET predictions in silico to establish a preliminary SAR. In vitro, we used BT474 (ER+HER2+) and MCF-7 (ER-HER2) cell lines along with normal breast cell epithelial cells (MFC-10a) for anticancer studies and EGFR kinase inhibition assay studies. As the Reactive Oxygen Species (ROS) plays the main role in cancer development, we also analyzed the antioxidant potentials of these compounds.

Results: Among the family of eleven amides substituted (Z)-β-amino enones (5a-k), compounds 5b, 5c, 5g, and 5h showed valuable in silico and in vitro bio-activity. Remarkably, the in-silico results almost coincided with in vitro study results.

Conclusion: We recommend compounds 5b, 5c, 5g, and 5h for pre-clinical and clinical evaluation to establish them as future cancer therapeutics.

Background: Acinetobacter baumannii is one of the main causes of nosocomial infections. No vaccine has yet been licensed for use in humans, and efforts are still ongoing.

Objective: In the present study, we have predicted the B-cell epitopes of A. baumannii's outer membrane protein K (OMPK) by using epitope prediction algorithms as possible vaccine candidates for future studies.

Methods: The linear B-cell epitopes were predicted by seven different prediction tools. The 3D structure of OMPK was modeled and used for discontinuous epitope prediction by ElliPro and DiscoTope 2.0 tools. The final linear epitopes and the discontinuous epitope segments were checked for potential allergenicity, toxicity, human similarity, and experimental records. The structure and physicochemical features of the final epitopic peptide were assessed by numerous bioinformatics tools.

Results: Many B-cell epitopes were detected that could be assessed for possible antigenicity and immunogenicity. Also, an epitopic 22-mer region (peptide) of OMPK was found that contained both linear and discontinuous B-cell epitopes. This epitopic peptide has been found to possess appropriate physicochemical and structural properties to be an A. baumannii vaccine candidate.

Conclusion: Altogether, here, the high immunogenic B-cell epitopes of OMPK have been identified, and a high immunogenic 22-mer peptide as an A. baumannii vaccine candidate has been introduced. The in vitro/in vivo studies of this peptide are recommended to decide its real efficacy and efficiency.

Background: The development of polymeric-based drug delivery has seen faster growth in the past two decades. In polymers, copolymers as drug carriers are increasing to decrease the drug compounds' side effects and dosage-related toxicity.

Objectives: The study's primary objective is to utilize computational resources to design drug molecules and perform in silco physicochemical property analysis. In our study, we designed new copolymers based on N-(2-Hydroxypropyl) methacrylamide (HPMA) as backbone along with polyethylene glycol (PEG) and lauryl methacrylate (LMA).

Methods: Different functional groups were selected for attaching to the side chain of the copolymers through a random trial and error approach. In order to predict the pharmacokinetic properties (absorption, distribution, metabolism, excretion, and toxicity), the designed copolymer molecules were evaluated utilizing ADME and PkCSM pharmacokinetics servers. Molecular interaction between the designed copolymer molecules and human serum albumin (HSA) was performed using AutoDock Vina and PatchDock server.

Results: The designed molecules are shown to be soluble in water and have high gastrointestinal absorption. Only one molecule is predicted to pass through the blood-brain barrier. Two designed molecules have been shown to have carcinogenic properties. Lethal dose 50 (LD50), cytochrome P450, and permeability glycoprotein Enzyme's substrate formation were also analyzed for toxicity and metabolism.

Conclusion: Our study will provide insight for designing new drug compounds or carriers and analyzing their physicochemical properties to help further optimize compounds for clinical studies.