Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking
- Title
- Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking
- Creator
- Nag A.; Dhull N.; Gupta A.
- Description
- Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcalmol?1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcalmol?1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligandprotein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10kcalmol?1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition. Graphical abstract: [Figure not available: see fulltext.]. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
- Source
- Molecular Diversity, Vol-27, No. 1, pp. 487-509.
- Date
- 2023-01-01
- Publisher
- Institute for Ionics
- Subject
- Diseases; Molecular docking; Network pharmacology; Phytochemicals; Simulation; Statistics; Tea
- Coverage
- Nag A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, India; Dhull N., Department of Life Sciences, Christ (Deemed to be University), Bangalore, India; Gupta A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, India
- Rights
- All Open Access; Bronze Open Access; Green Open Access
- Relation
- ISSN: 13811991; PubMed ID: 35536529; CODEN: MODIF
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Nag A.; Dhull N.; Gupta A., “Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14411.