Current computer-aided drug design最新文献

Background: Translationally controlled tumour protein (TCTP) is associated with tumor diseases, such as breast cancer, and its inhibitor can reduce the growth of tumor cells. Unfortunately, there is currently no effective medication available for treating TCTP-related breast cancer.

Objective: The objective of this study was to explore the inhibitor candidates among natural compounds for the treatment of breast cancer related to TCTP protein.

Methods: To explore the potential inhibitors of TCTP, we first screened out four potential inhibitors in the Traditional Chinese Medicine (TCM) for cancer based on AI virtual screening using the docking method, and then revealed the interaction mechanism of TCTP and four candidate inhibitors from TCM with molecular docking and molecular dynamics (MD) methods.

Results: Based on the conformational characteristics and the MD properties of the four leading compounds, we designed the new skeleton molecules with the AI method using MolAICal software. Our MD simulations have revealed that different small molecules bind to different sites of TCTP, but the flexible regions and the signaling pathways are almost the same, and the VDW and hydrophobic interactions are crucial in the interactions between TCTP and ligands.

Conclusion: We have proposed the candidate inhibitor of TCTP. Our study has provided a potential new method for exploring inhibitors from Traditional Chinese Medicine (TCM).

Background: Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer lacking specific receptors, with dysregulated and overactivated Hedgehog (Hh) and mTOR/PI3K/AKT signaling pathways as potential therapeutic targets.

Objective: This study aimed to identify potential inhibitors among 53 alkaloids derived from 9 marine bryozoans using in silico approaches. It sought to analyze their impact on key signaling targets and their potential for future experimental validation.

Methods: In this research, selected targets were evaluated for protein-protein interactions, coexpression survival, and expression profiles. The protein expression was validated through the Human Protein Atlas (HPA) database and druggability through DGIdb. Online web servers were employed to assess drug-likeness, physiochemical properties, pharmacokinetics, and toxicological characteristics of the compounds. Molecular docking and dynamic simulations were carried out for ligand-protein interactions. Common Pharmacophore features, bioavailability, bioactivity, and biological activity spectrum (BAS) were also analyzed.

Results: Out of the 13 compounds studied, 10 displayed strong binding affinity with binding energies ranging from >-6.5 to <-8 Kcal/mol across all targets. Molecular dynamics simulations provided insights into Amathamide E's stability and conformational changes. Pharmacophore modeling revealed common features in 14 compounds potentially responsible for their biological activity.

Conclusion: Our findings indicate the potential of marine-derived compounds as TNBC inhibitors. Further in vitro and in vivo validation is necessary to establish their effectiveness and explore their role as novel anti-TNBC agents.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a causative agent for multiple drug-resistant diseases and is a prime health concern. Currently, antibiotics like vancomycin, daptomycin, fluoroquinolones, linezolid, fifth-generation cephalosporin and others are available in the market for the treatment of MRSA infection.

Methods: With the increasing prevalence of drug-resistant cases, researchers are actively investigating alternative strategies to combat MRSA, including the exploration of peptide therapeutics. This study employed computational methods to prospect for potential Antimicrobial Peptides (AMPs).

Results: A total of One hundred and fifty antimicrobial peptides were explored based on physicochemical properties. The results showed that Clavanin B was the most appropriate candidate. Molecular Docking and Molecular Dynamics Simulation results showed the protein-peptide interaction of the MRSA target proteins, Penicillin Binding Protein 2a and Panton-Valentine Leukocidin Toxin, with the Antimicrobial Peptide Clavanin B.

Conclusion: Currently, the antimicrobial peptide database highlights Clavanin B's role as an anti-HIV peptide. Moreover, this investigation proposes Clavanin B as a viable repurposed drug for treating MRSA, underscoring its potential deployment in the management of MRSA infections.

Aims and objectives: This study aimed to evaluate the pharmacological mechanism of Hederagenin (HD) combined with oxaliplatin (L-OHP) in treating gastric cancer (GC) through network pharmacology combined with experimental verification.

Material and methods: Network pharmacology methods were used to screen potential targets for HD, L-OHP, and GC-related targets from public databases, and the intersection of the three gene sets was taken. Cross genes were analyzed through protein-protein interaction (PPI) networks to predict core targets, and related pathways were predicted through GO and KEGG enrichment analysis. The experimental results were verified by the in vitro experiments. HD was applied on AGS/L-OHP cells, and then cellular chemosensitivity and the expressions of P-gp, Survivin, Bcl-2, p-Akt, and p-PI3K genes were detected. Wound assay and Transwell Chamber assay were employed to detect the effect of HD on AGS/L-OHP cells. Nude mice xenograft models transfected using AGS/L-OHP cells were also treated with HD in order to verify the results. The size and weight of the tumor, as well as the expressions of P-gp, Survivin, Bcl-2, p- Akt and p-PI3K genes, were also measured.

Results: KEGG analysis showed that the anti-gastric cancer effect of HD was mediated mainly by PI3K-Akt signaling pathways. The PI3K-Akt signaling pathway containing more enriched genes may play a greater role in anti-gastric cancer. It was observed that for AGS/L-OHP cells jointly treated with HD and L-OHP, their activity, migration and invasion were significantly lower than those treated only using HD or L-OHP group. Moreover, expressions of p-Akt, p- PI3K, Bcl-2, P-gp, and Survivin for the HD+L-OHP group decreased significantly. Results of the in vivo experiments showed that the sizes and weights of tumors in the HD+L-OHP group were the lowest compared to the HD group and L-OHP group.

Conclusion: Our findings suggest that HD may reduce the resistance of AGS/L-OHP cells to LOHP by regulating the PI3K/Akt signaling pathway.

Background: Dapagliflozin is commonly used to treat type 2 diabetes mellitus (T2DM). However, research into the specific anti-T2DM mechanisms of dapagliflozin remains scarce.

Objective: This study aimed to explore the underlying mechanisms of dapagliflozin against T2DM.

Methods: Dapagliflozin-associated targets were acquired from CTD, SwissTargetPrediction, and SuperPred. T2DM-associated targets were obtained from GeneCards and DigSee. VennDiagram was used to obtain the overlapping targets of dapagliflozin and T2DM. GO and KEGG analyses were performed using clusterProfiler. A PPI network was built by STRING database and Cytoscape, and the top 30 targets were screened using the degree, maximal clique centrality (MCC), and edge percolated component (EPC) algorithms of CytoHubba. The top 30 targets screened by the three algorithms were intersected with the core pathway-related targets to obtain the key targets. DeepPurpose was used to evaluate the binding affinity of dapagliflozin with the key targets.

Results: In total, 155 overlapping targets of dapagliflozin and T2DM were obtained. GO and KEGG analyses revealed that the targets were primarily enriched in response to peptide, membrane microdomain, protein serine/threonine/tyrosine kinase activity, PI3K-Akt signaling pathway, MAPK signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. AKT1, PIK3CA, NOS3, EGFR, MAPK1, MAPK3, HSP90AA1, MTOR, RELA, NFKB1, IKBKB, ITGB1, and TP53 were the key targets, mainly related to oxidative stress, endothelial function, and autophagy. Through the DeepPurpose algorithm, AKT1, HSP90AA1, RELA, ITGB1, and TP53 were identified as the top 5 anti-targets of dapagliflozin.

Conclusion: Dapagliflozin might treat T2DM mainly by targeting AKT1, HSP90AA1, RELA, ITGB1, and TP53 through PI3K-Akt signaling.

Background: Alzheimer's disease is a type of dementia that affects neuronal function, leading to a decline in cognitive functions. Serotonin-6 (5HT6) receptors are implicated in the etiology of neurological diseases. 5HT6 receptor antagonists act as anti-dementia agents.

Pdb id: 7YS6 represents a membrane protein, and amplification and overexpression of this protein are associated with Alzheimer's disease. Coumarin-fused phenothiazines are significant anti-Alzheimer's agents due to their inhibitory activity on the Serotonin- 6 receptor.

Objectives: Numerous previously unreported Coumarin-substituted Phenothiazines [A2 to A50] were designed using in-silico methods to evaluate their 5HT6 receptor antagonistic activity. Molecular modeling techniques were employed to study the ligands [A2 to A50] in interaction with the Serotonin-6 receptor (PDB ID: 7YS6) using Schrödinger Suite 2019-4.

Methods: Molecular modeling studies of the designed ligands [A2 to A50] were conducted using the Glide module. In-silico ADMET screening was performed using the QikProp module, and binding free energy calculations were carried out using the Prime MM-GBSA module within the Schrödinger Suite. The binding affinity of the designed ligands [A2 to A50] towards 5HT6 receptors was determined based on Glide scores. Subsequently, ligand A31 underwent a 100 ns molecular dynamics simulation using the Desmond module of Schrödinger Suite 2020-1, which is based in New York, NY.

Results: The majority of the designed ligands exhibited strong hydrogen bonding interactions and hydrophobic associations with the serotonin-6 receptor, which hinder its activity. These ligands achieved remarkable Glide scores within the range of -4.2859 to -7.7128, in comparison to reference standards such as Idalopirdine (-7.78149), Intepirdine (-5.20103), Latrepirdine (-5.54853), and the co-crystallized ligand (-7.02889). In-silico ADMET properties for these ligands fell within the recommended values for drug-likeness. It is worth noting that the MM-GBSA binding free energy of the most potent inhibitor was positive, indicating a strong binding interaction. Additionally, the dynamic behavior of the protein (7YS6)-ligand (A31) complex was studied by subjecting ligand A31 to a 100 ns molecular dynamics simulation.

Conclusion: The results of this study reveal strong evidence supporting the potential of coumarin- substituted phenothiazine derivatives as effective Serotonin-6 receptor antagonists. Ligands [A2 to A50], which exhibited noteworthy Glide scores, hold promise for significant anti- Alzheimer activity. Further in-vitro and in-vivo investigations are warranted to explore and confirm their therapeutic potential.

Background: Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges.

Methods: To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format.

Results: PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing largescale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research.

Conclusion: PyComp effectively addresses some of the most pressing challenges in highthroughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds.

Background: SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest.

Objective: This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations.

Methods: An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted.

Results: Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors.

Conclusion: The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

Introduction: Inflammatory bowel disease (IBD) has become one of the public problems worldwide and its incidence rate is increasing year by year. Its concomitant disease i.e. diabetes mellitus (DM) has attracted more and more attention due to DM altering the progression of IBD and leading to long periods of intermittent recurrence and deterioration. The common mechanism and potential target drug of IBD with comorbid chronic conditions of DM were explored.

Methods: Gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were identified by R software. GO annotation and pathway enrichment were performed, a protein-protein interaction (PPI) network was constructed, associated lncRNAs were predicted and drug prediction targeting key genes was made. Additionally, the regulatory network among core genes, associated pathways, and predicted lncRNA in IBD with coexistent DM were visualized.

Results: We identified the critical gene MMP3 with lncRNA CDKN2BAS involved in the PPAR pathway, which uncovered the underlying regulatory mechanism of IBD with coexistent DM. We also predicted the potential therapeutic compound ZINC05905909 acting on MMP3.

Conclusion: Our findings revealed the regulatory mechanism chain of critical gene MMP3, lncRNA CDKN2BAS, and PPAR pathway and provided potential therapeutic compound ZINC05905909 for drug therapy to treat comorbid IBD DM.

Background: Hyperlipidemia, a cardiovascular disease risk factor, is characterized by a rise in low-density lipoprotein (LDL), triglycerides and total cholesterol, and a decrease in high-density lipoprotein (HDL). Cholesteryl ester transfer protein (CETP) enables the transfer of cholesteryl ester from HDL to LDL and very low-density lipoprotein.

Objectives: CETP inhibition is a promising approach to prevent and treat cardiovascular diseases. By inhibiting lipid transport activity, it increases HDL levels and decreases LDL levels.

Materials and method: Herein, diaryl sulfonamides 6a-6g and 7a-7g were prepared, and the structure of these compounds was fully determined using different spectroscopic techniques.

Results: These compounds underwent biological evaluation in vitro and showed different inhibitory activities against CETP; 100% inhibitory activity was observed for compounds 7a-7g, while activities of compounds 6a-6g ranged up to 42.6% at 10 μM concentration. Pharmacophore mapping agreed with the bioassay results where the four aromatic ring compounds 7a-7g possessed higher fit values against Hypo4/8 and the shape-complemented Hypo4/8 in comparison to compounds 6a-6g.

Conclusion: Docking of the synthesized compounds using libdock and ligandfit engines revealed that compounds 7a-7g formed п-п stacking and hydrophobic interactions with the binding pocket, while compounds 6a-6g missed these hydrophobic interactions with amino acids Leu206, Phe265, and Phe263.