Binding affinity analysis and ADMET prediction of epigallocatechine gallate (EGCG) derivatives for AP-1 protein: a drug target for liver cancer

Sagar, Mamta; Pathak, Rajesh Kumar; Pandey, Rishi Kumar; Singh, Dev Bukhsh; Pandey, Neetesh; Gupta, Manish Kumar

doi:10.1007/s13721-014-0066-x

Cited by 9 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The activator protein-1 (AP-1) could regulate the transformation of tumor cells and the EGCG showed inhibitory effect on AP-1 protein . In addition, the EGCG acetylated derivatives have greater affinity with AP-1 protein on the target site of Ser 278, Asp 163, Asp 170, Arg 281, and Arg 288 by hydrogen bonds interactions . The inhibition of AP-1 protein could further up regulate the tumor suppressor gene p53 along with the reducing effect on tumor activity.…”

Section: Biological Activitiesmentioning

confidence: 99%

“…40 In addition, the EGCG acetylated derivatives have greater affinity with AP-1 protein on the target site of Ser 278, Asp 163, Asp 170, Arg 281, and Arg 288 by hydrogen bonds interactions. 41 The inhibition of AP-1 protein could further up regulate the tumor suppressor gene p53 along with the reducing effect on tumor activity. Due to the crosstalk between GTP-induced cell signaling pathways, GTE or GTP were both reported to effectively prevent the early stages of cancer, while the combined use of EGCG, EGCG derivatives and anticancer drugs exert synergistic inhibition on the proliferation of CSC, which altogether represent promising therapeutic approaches for human cancers.…”

Section: ■ Biological Activitiesmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in the Understanding of the Health Benefits and Molecular Mechanisms Associated with Green Tea Polyphenols

Xing

Zhang

et al. 2019

J. Agric. Food Chem.

397

250

View full text Add to dashboard Cite

Tea, leaf, or bud from the plant Camellia sinensis, make up some of the beverages popularly consumed in different parts of the world as green tea, oolong tea, or black tea. More particularly, as a nonfermented tea, green tea has gained more renown because of the significant health benefits assigned to its rich content in polyphenols. As a main constituent, green tea polyphenols were documented for their antioxidant, anti-inflammation, anticancer, anticardiovascular, antimicrobial, antihyperglycemic, and antiobesity properties. Recent reports demonstrate that green tea may exert a positive effect on the reduction of medical chronic conditions such as cardiovascular disease, cancer, Alzheimer’s disease, Parkinson’s disease, and diabetes. The health benefits of green teas, in particular EGCG, are widely investigated, and these effects are known to be primarily associated with the structure and compositions of its polyphenols. This Review focuses on the diverse constituents of green tea polyphenols and their molecular mechanisms from the perspective of their potential therapeutic function. Recent advances of green tea polyphenols on their bioavailability, bioaccessibility, and microbiota were also summarized in this article. Dietary supplementation with green tea represents an attractive alternative toward promoting human health.

show abstract

Section: Biological Activitiesmentioning

confidence: 99%

Section: ■ Biological Activitiesmentioning

confidence: 99%

Recent Advances in the Understanding of the Health Benefits and Molecular Mechanisms Associated with Green Tea Polyphenols

Xing

Zhang

et al. 2019

J. Agric. Food Chem.

397

250

View full text Add to dashboard Cite

show abstract

“…Ligand optimization is required to balance the physicochemical properties and pharmacokinetics of a ligand so that it can qualify for the criteria of the candidate drug. Docking tools evaluate the binding affinity and interaction of ligands with the target binding sites and help screen some top‐scoring ligands from a vast set of chemical structures (Sagar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches and their applications in drug discovery and design

et al. 2022

Self Cite

View full text Add to dashboard Cite

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

show abstract