[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
Submission ::
Ethics::
Registration::
Contact us::
Site Facilities::
::
Impact Factor
Impact Factor 2024: 1.5
5-Year Impact Factor: 2.2
Cite Score 2023: 3.6
SJR 2023: 0.573
SNIP 2022: 0.479

 
..
Publication Fee
..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 12, Issue 4 (Int J Mol Cell Med 2023) ::
Int J Mol Cell Med 2023, 12(4): 372-387 Back to browse issues page
In Silico Molecular Docking of Phytochemicals for Type 2 Diabetes Mellitus Therapy: A Network Pharmacology Approach
Sooriyakala Rani Sri Prakash1 , Sree Meenakshi Kamalnath1 , Arul Jayanthi Antonisamy 2, Sivasankari Marimuthu1 , Sankar Malayandi1
1- Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India.
2- Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India. , aruljayanthi@mepcoeng.ac.in
Abstract:   (619 Views)
Identification of potential lead molecules in herbal medicines is crucial not only for validation but also for drug discovery. This study was focused on identifying the therapeutic mechanisms of 10 common herbs used to treat type 2 diabetes mellitus (T2DM) using network pharmacology and docking studies. Details pertaining to medicinal plants and their phytoconstituents were obtained from Indian Medicinal Plants, Phytochemistry, and Therapeutics and Dr. Duke’s database, respectively. MolSoft was used to assess their drug likeness. Prediction of protein targets for the screened phytochemicals and the list of target genes involved in T2DM were obtained using Swiss TargetPrediction and GeneCards respectively. STRING; Cytoscape; Database for Annotation, Visualization, and Integrated Discovery; and PyRx were used for network construction, network analysis, gene ontology analysis, and molecular docking, respectively. The protein targets MAPK1, AKT1, PI3K, and EGFR were identified to play a crucial role in the progression of T2DM. Furthermore, molecular docking indicated that nimbaflavone exhibited high binding affinities for MAPK1 (−8.7 kcal/mole) and PI3K (−9.6 kcal/mole), whereas rutin and 10-hydroxyaloin-B showed high binding affinities for AKT1 (−7.4 kcal/mole) and EGFR (−8.1 kcal/mole), respectively. The findings from this study suggest that flavonoids are the major phytoconstituents that display antidiabetic activity by interacting with key protein molecules related to the MAPK and PI3K-AKT signaling pathways, thereby aiding in the treatment of T2DM. The activation of these pathways alters Ras-GTPase activity and enhances the expression of GLUT4, a glucose transporter, resulting in the uptake of glucose from the bloodstream.
Keywords: Type 2 diabetes mellitus, network pharmacology, gene ontology, molecular docking
Full-Text [PDF 1300 kb]   (278 Downloads)    
Type of Study: Original Article | Subject: Bioinformatic
Received: 2023/01/29 | Accepted: 2024/02/5 | Published: 2024/05/20
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML     Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rani Sri Prakash S, Kamalnath S M, Antonisamy A J, Marimuthu S, Malayandi S. In Silico Molecular Docking of Phytochemicals for Type 2 Diabetes Mellitus Therapy: A Network Pharmacology Approach. Int J Mol Cell Med 2023; 12 (4) :372-387
URL: http://ijmcmed.org/article-1-2091-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 4 (Int J Mol Cell Med 2023) Back to browse issues page
International Journal of Molecular and Cellular Medicine (IJMCM) International Journal of Molecular and Cellular Medicine (IJMCM)
Persian site map - English site map - Created in 0.05 seconds with 38 queries by YEKTAWEB 4660