Current computer-aided drug design最新文献

Background: A network pharmacology study on the biological action of ginseng in the treatment of colorectal cancer (CRC) by regulating the tumor microenvironment (TME).

Objectives: To investigate the potential mechanism of action of ginseng in the treatment of CRC by regulating TME.

Methods: This research employed network pharmacology, molecular docking techniques, and bioinformatics validation. Firstly, the active ingredients and the corresponding targets of ginseng were retrieved using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Traditional Chinese Medicine Integrated Database (TCMID), and the Traditional Chinese Medicine Database@Taiwan (TCM Database@Taiwan). Secondly, the targets related to CRC were retrieved using Genecards, Therapeutic Target Database (TTD), and Online Mendelian Inheritance in Man (OMIM). Tertiary, the targets related to TME were derived from screening the GeneCards and National Center for Biotechnology Information (NCBI)-Gene. Then the common targets of ginseng, CRC, and TME were obtained by Venn diagram. Afterward, the Protein-protein interaction (PPI) network was constructed in the STRING 11.5 database, intersecting targets identified by PPI analysis were introduced into Cytoscape 3.8.2 software cytoHubba plugin, and the final determination of core targets was based on degree value. The OmicShare Tools platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the core targets. Autodock and PyMOL were used for molecular docking verification and visual data analysis of docking results. Finally, we verified the core targets by Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases in bioinformatics.

Results: A total of 22 active ingredients and 202 targets were identified to be closely related to the TME of CRC. PPI network mapping identified SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 as possible core targets. Go enrichment analysis showed that it was mainly involved in T cell co-stimulation, lymphocyte co-stimulation, growth hormone response, protein input, and other biological processes; KEGG pathway analysis found 123 related signal pathways, including EGFR tyrosine kinase inhibitor resistance, chemokine signaling pathway, VEGF signaling pathway, ErbB signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. The molecular docking results showed that the main chemical components of ginseng have a stable binding activity to the core targets. The results of the GEPIA database showed that the mRNA levels of PIK3R1 were significantly lowly expressed and HSP90AA1 was significantly highly expressed in CRC tissues. Analysis of the relationship between core target mRNA levels and the pathological stage of CRC showed that the levels of SRC changed si

Background: Ulcerative colitis (UC) and irritable bowel syndrome (IBS) are common intestinal diseases. According to the clinical experience and curative effect, the authors formulated Kuiyu Pingchang Decoction (KYPCD) comprised of Paeoniae radix alba, Aurantii Fructus, Herba euphorbiae humifusae, Lasiosphaera seu Calvatia, Angelicae sinensis radix, Panax ginseng C.A. Mey., Platycodon grandiforus and Allium azureum Ledeb.

Objective: The aim of the present study was to explore the mechanisms of KYPCD in the treatment of UC and IBS following the Traditional Chinese Medicine (TCM) theory of "Treating different diseases with the same treatment".

Methods: The chemical ingredients and targets of KYPCD were obtained using the Traditional Chinese Medicine Systems Pharmacology database and analysis platform (TCMSP). The targets of UC and IBS were extracted using the DisGeNET, GeneCards, DrugBANK, OMIM and TTD databases. The "TCM-component-target" network and the "TCM-shared target-disease" network were imaged using Cytoscape software. The protein-protein interaction (PPI) network was built using the STRING database. The DAVID platform was used to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using Autodock Tools software, the main active components of KYPCD were molecularly docked with their targets and visualized using PyMOL.

Results: A total of 46 active ingredients of KYPCD corresponding to 243 potential targets, 1,565 targets of UC and 1,062 targets of IBS, and 70 targets among active ingredients and two diseases were screened. Core targets in the PPI network included IL6, TNF, AKT1, IL1B, TP53, EGFR and VEGFA. GO and KEGG enrichment analysis demonstrated 563 biological processes, 48 cellular components, 82 molecular functions and 144 signaling pathways. KEGG enrichment results revealed that the regulated pathways were mainly related to the PI3K-AKT, MAPK, HIF-1 and IL-17 pathways. The results of molecular docking analysis indicated that the core active ingredients of KYPCD had optimal binding activity to their corresponding targets.

Conclusion: KYPCD may use IL6, TNF, AKT1, IL1B, TP53, EGFR and VEGFA as the key targets to achieve the treatment of UC and IBS through the PI3K-AKT, MAPK, HIF-1 and IL-17 pathways.

Background: Due to the biological importance of the benzoxazole derivatives, some 1- (benzo[d]oxazol-2-yl)-3,5-diphenyl-formazans 4a-f were synthesized and screened for in-silico studies and in-vitro antibacterial activity.

Methods: The benzo[d]oxazole-2-thiol (1) was prepared by reacting with 2-aminophenol and carbon disulfide in the presence of alcoholic potassium hydroxide. Then 2-hydrazinylbenzo[d] oxazole (2) was synthesized from the reaction of compound 1 with hydrazine hydrate in the presence of alcohol. Compound 2 was reacted with aromatic aldehydes to give Schiff base, 2-(2- benzylidene-hydrazinyl)benzo[d]oxazole derivatives 3a-f. The title compounds, formazan derivatives 4a-f, were prepared by a reaction of benzene diazonium chloride. All compounds were confirmed by their physical data, FTIR, ¹H-NMR, and ¹³CNMR spectral data. All the prepared title compounds were screened for in-silico studies and in-vitro antibacterial activity on various microbial strains.

Results: Molecular docking against the 4URO receptor demonstrated that molecule 4c showed a maximum dock score of (-) 8.0 kcal/mol. MD simulation data reflected the stable ligand-receptor interaction. As per MM/PBSA analysis, the maximum free binding energy of (-) 58.831 kJ/mol was exhibited by 4c. DFT calculation data confirmed that most of the molecules were soft molecules with electrophilic nature.

Conclusion: The synthesized molecules were validated using molecular docking, MD simulation, MMPBSA analysis, and DFT calculation. Among all the molecules, 4c showed maximum activity. The activity profile of the synthesized molecules against tested micro-organisms was found to be 4c>4b>4a>4e>4f>4d.

Background: Drug-resistant Staphylococcus aureus (S. aureus) has spread from nosocomial to community-acquired infections. Novel antimicrobial drugs that are effective against resistant strains should be developed. S. aureus tyrosyl-tRNA synthetase (saTyrRS) is considered essential for bacterial survival and is an attractive target for drug screening.

Objectives: The purpose of this study was to identify potential new inhibitors of saTyrRS by screening compounds in silico and evaluating them using molecular dynamics (MD) simulations.

Methods: A 3D structural library of 154,118 compounds was screened using the DOCK and GOLD docking simulations and short-time MD simulations. The selected compounds were subjected to MD simulations of a 75-ns time frame using GROMACS.

Results: Thirty compounds were selected by hierarchical docking simulations. The binding of these compounds to saTyrRS was assessed by short-time MD simulations. Two compounds with an average value of less than 0.15 nm for the ligand RMSD were ultimately selected. The longtime (75 ns) MD simulation results demonstrated that two novel compounds bound stably to saTyrRS in silico.

Conclusion: Two novel potential saTyrRS inhibitors with different skeletons were identified by in silico drug screening using MD simulations. The in vitro validation of the inhibitory effect of these compounds on enzyme activity and their antibacterial effect on drug-resistant S. aureus would be useful for developing novel antibiotics.

Background: Hyperlipidemia is considered a major risk factor for the progress of atherosclerosis.

Objective: Cholesteryl ester transfer protein (CETP) facilitates the relocation of cholesterol esters from HDL to LDL. CETP inhibition produces higher HDL and lower LDL levels.

Methods: Synthesis of nine benzylamino benzamides 8a-8f and 9a-9c was performed.

Results: In vitro biological study displayed potential CETP inhibitory activity, where compound 9c had the best activity with an IC₅₀ of 1.03 μM. Induced-fit docking demonstrated that 8a-8f and 9a-9c accommodated the CETP active site and hydrophobic interaction predominated ligand/ CETP complex formation.

Conclusion: Pharmacophore mapping showed that this scaffold endorsed CETP inhibitors features and consequently elaborated the high CETP binding affinity.

Background: Epilepsy is a chronic neurological disorder caused by irregular electrical activity in the brain. To manage this disorder effectively, it is imperative to identify potential pharmacological targets and to understand the pathophysiology of epilepsy in depth.

Objective: This research aimed to identify promising leads from a library of 1,2,4-triazine-6Hindolo[ 2,3-b]quinoline derivatives and optimize them using in silico and dynamic processes.

Methods: We used computational studies to examine 1,2,4-Triazine-6H-indolo[2,3-b]quinoline derivatives. Some methods were used to strengthen the stability of binding sites, including Docking, ADMET, IFD, MMGBSA, Density Functional Theory (DFT), and Molecular Dynamics.

Results: HRSN24 and HRSN34 exhibited promising pharmacokinetic and pharmacodynamic characteristics compared to standard drugs (Carbamazepine and Phenytoin) and a co-crystal ligand (Diazepam). Both HRSN24 and HRSN34 presented notable Glide Xp docking scores (-4.528 and -4.633 Kcal/mol), IFD scores (-702.22 and -700.3 Kcal/mol), and MMGBSA scores (-45.71 and -14.46 Kcal/mol). HRSN24 was selected for molecular dynamics and DFT analysis. During MD, HRSN24 identified LYS21, GLY22, ASP24, ARG26, VAL53, MET55, and SER308 as the most important amino acid residues for hydrophobic interactions. A DFT computation was performed to determine the physicochemical properties of HRSN24, revealing a total energy of -1362.28 atomic units, a HOMO value of -0.20186, and a LUMO value of -0.01915.

Conclusion: Based on computational modelling techniques, an array of 1,2,4-triazine-6H-indolo [2,3-b]quinoline derivatives were evaluated for their anti-convulsant properties. A stable compound within the GABAA receptor was identified by HRSN24, suggesting its affinity as an anti-convulsant.

Aims: In this work, some new chromeno[4',3'-b]pyrano[6,5-d]pyrimidines,3-amino and 3-methyl-5-aryl-4-imino-5(H)-chromeno[4',3'-b]pyrano[6,5-d]pyrimidine-6-ones derivatives were synthesized.

Background: Chromenopyrimidines have attracted significant attention recently because of their activities, such as antiviral and cytotoxic activity.

Objective: All synthesized compounds were characterized using IR, ¹H-NMR, Mass Spectroscopy, and elemental analysis data.

Methods: Molecular docking studies were carried out to determine the inhibitory action of studied ligands against the Main Protease (6LU7, 6m03) of coronavirus (COVID-19). Moreover, the Lipinski Rule parameters were calculated for the synthesized compounds.

Results: The result of the docking studies showed a significant inhibitory action against the Main protease (M^pro) of SARS-CoV-2, and the binding energy (ΔG) values of the ligands against the protein (6LU7, 6M03) are -7.8 to -9.9 Kcal/mole.

Conclusion: It may conclude that some ligands were likely to be considered lead-like against the main protease of SARS-CoV-2.

Aims: This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.

Background: The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.

Objectives: The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.

Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.

Results: Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.

Conclusion: PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.

Introduction: This study aimed to determine the in vitro and in silico effects of some natural and synthetic molecules on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and α-glucosidase enzymes.

Background: Alzheimer's disease (AD) and Type II diabetes mellitus (T2DM) are considered the most important diseases of today's world. However, the side effects of therapeutic agents used in both diseases limit their use. Therefore, developing drugs with high therapeutic efficacy and better pharmacological profile is important.

Objectives: This study sets out to determine the related enzyme inhibitors used in treating AD and T2DM, considered amongst the most important diseases of today's world.

Methods: In the current study, the in vitro and in silico effects of dienestrol, hesperetin, Lthyroxine, 3,3',5-Triiodo-L-thyronine (T3) and dobutamine molecules on AChE, BChE and α - glycosidase enzyme activities were investigated.

Results: All the molecules showed an inhibitory effect on the enzymes. The IC₅₀ and K_i values of the L-Thyroxine molecule, which showed the strongest inhibition effect for the AChE enzyme, were determined as 1.71 μM and 0.83 ± 0.195 μM, respectively. In addition, dienestrol, T3, and dobutamine molecules showed a more substantial inhibition effect than tacrine. The dobutamine molecule showed the most substantial inhibition effect for the BChE enzyme, and IC₅₀ and K_i values were determined as 1.83 μM and 0.845 ± 0.143 μM, respectively. The IC₅₀ and K_i values for the hesperetin molecule, which showed the strongest inhibition for the α -glycosidase enzyme, were determined as 13.57 μM and 12.33 ± 2.57 μM, respectively.

Conclusion: According to the results obtained, the molecules used in the study may be considered potential inhibitor candidates for AChE, BChE and α-glycosidase.

Background: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score.

Methods: Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification.

Results: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results.

Conclusion: Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.