scholarly journals Virtual screening for oseltamivir-resistant a (H5N1) influenza neuraminidase from traditional Chinese medicine database: a combined molecular docking with molecular dynamics approach

SpringerPlus ◽  
2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Vasudevan Karthick ◽  
Karuppasamy Ramanathan
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
H5n1 Influenza

scholarly journals In SilicoIdentification of Potent PPAR-γAgonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Kuan-Chung Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Molecular Dynamics ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
Prediction Models ◽  
Protein Complexes ◽  
Md Simulations ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptors ◽  
Key Residues

The peroxisome proliferator-activated receptors (PPARs) related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes. They are used to be served as therapeutic targets against metabolic disorder, such as obesity, dyslipidemia, and diabetes; especially, PPAR-γis the most extensively investigated isoform for the treatment of dyslipidemic type 2 diabetes. In this study, we filter compounds of traditional Chinese medicine (TCM) using bioactivities predicted by three distinct prediction models before the virtual screening. For the top candidates, the molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR-γprotein. The top two TCM candidates, 5-hydroxy-L-tryptophan and abrine, have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR-γprotein, such as residues Ser289 and Lys367. The secondary amine group of abrine also stabilized an H-bond with residue Ser289. From the figures of root mean square fluctuations (RMSFs), the key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control. Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR-γprotein.

scholarly journals A network pharmacology-based investigation on the bioactive ingredients and molecular mechanisms of Gelsemium elegans Benth against colorectal cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wancai Que ◽  
Maohua Chen ◽  
Ling Yang ◽  
Bingqing Zhang ◽  
Zhichang Zhao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanism ◽  
Enrichment Analysis ◽  
Dynamics Simulation ◽  
Network Pharmacology

Abstract Background Colorectal cancer (CRC) remains one of the leading causes of cancer-related death worldwide. Gelsemium elegans Benth (GEB) is a traditional Chinese medicine commonly used for treatment for gastrointestinal cancer, including CRC. However, the underlying active ingredients and mechanism remain unknown. This study aims to explore the active components and the functional mechanisms of GEB in treating CRC by network pharmacology-based approaches. Methods Candidate compounds of GEB were collected from the Traditional Chinese Medicine@Taiwan, Traditional Chinese Medicines Integrated Database, Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine, and published literature. Potentially active targets of compounds in GEB were retrieved from SwissTargetPrediction databases. Keywords “colorectal cancer”, “rectal cancer” and “colon cancer” were used as keywords to search for related targets of CRC from the GeneCards database, then the overlapped targets of compounds and CRC were further intersected with CRC related genes from the TCGA database. The Cytoscape was applied to construct a graph of visualized compound-target and pathway networks. Protein-protein interaction networks were constructed by using STRING database. The DAVID tool was applied to carry out Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis of final targets. Molecular docking was employed to validate the interaction between compounds and targets. AutoDockTools was used to construct docking grid box for each target. Docking and molecular dynamics simulation were performed by Autodock Vina and Gromacs software, respectively. Results Fifty-three bioactive compounds were successfully identified, corresponding to 136 targets that were screened out for the treatment of CRC. Functional enrichment analysis suggested that GEB exerted its pharmacological effects against CRC via modulating multiple pathways, such as pathways in cancer, cell cycle, and colorectal cancer. Molecular docking analysis showed that the representative compounds had good affinity with the key targets. Molecular dynamics simulation indicated that the best hit molecules formed a stable protein-ligand complex. Conclusion This network pharmacology study revealed the multiple ingredients, targets, and pathways synergistically involved in the anti-CRC effect of GEB, which will enhance our understanding of the potential molecular mechanism of GEB in treatment for CRC and lay a foundation for further experimental research.

scholarly journals Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Wang ◽  
Minghui Wan ◽  
Dongjiang Liao ◽  
Guilin Peng ◽  
Xin Xu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Natural Products ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
Md Simulation ◽  
Interaction Analysis ◽  
Docking Simulation ◽  
Building Blocks ◽  
Intracellular Channel

Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition.

scholarly journals Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation

2021 ◽  
Vol 12 ◽  
Author(s):  
Baoyue Zhang ◽  
Jun Zhao ◽  
Zhe Wang ◽  
Pengfei Guo ◽  
Ailin Liu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
Cell Model ◽  
Chemical Constituents ◽  
New Drugs ◽  
Molecular Docking Study ◽  
Biological Validation

Alzheimer’s disease (AD) is a neurodegenerative disease that seriously threatens the health of the elderly. At present, no drugs have been proven to cure or delay the progression of the disease. Due to the multifactorial aetiology of this disease, the multi-target-directed ligand (MTDL) approach provides an innovative and promising idea in search for new drugs against AD. In order to find potential multi-target anti-AD drugs from traditional Chinese medicine (TCM) formulae, a compound database derived from anti-AD Chinese herbal formulae was constructed and predicted by the anti-AD multi-target drug prediction platform established in our laboratory. By analyzing the results of virtual screening, 226 chemical constituents with 3 or more potential AD-related targets were collected, from which 16 compounds that were predicted to combat AD through various mechanisms were chosen for biological validation. Several cell models were established to validate the anti-AD effects of these compounds, including KCl, Aβ, okadaic acid (OA), SNP and H2O2 induced SH-SY5Y cell model and LPS induced BV2 microglia model. The experimental results showed that 12 compounds including Nonivamide, Bavachromene and 3,4-Dimethoxycinnamic acid could protect model cells from AD-related damages and showed potential anti-AD activity. Furthermore, the potential targets of Nonivamide were investigated by molecular docking study and analysis with CDOCKER revealed the possible binding mode of Nonivamide with its predicted targets. In summary, 12 potential multi-target anti-AD compounds have been found from anti-AD TCM formulae by comprehensive application of computational prediction, molecular docking method and biological validation, which laid a theoretical and experimental foundation for in-depth study, also providing important information and new research ideas for the discovery of anti-AD compounds from traditional Chinese medicine.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

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