Network Pharmacology Integrated With Quantum-Polarized Ligand Docking and Molecular Simulation Revealed the Anti-Diabetic Potential of Curcumin

被引:0
作者
Khan, Abbas [1 ]
Sayaf, Abrar Mohammad [2 ]
Alshammarri, Abdalrahman [3 ]
Zahid, Muhammad Ammar [1 ]
Al-Zoubi, Raed M. [4 ,5 ,6 ]
Shkoor, Mohanad [7 ]
Benameur, Tarek [8 ]
Wei, Dong-Qing [10 ]
Agouni, Abdelali [1 ,9 ]
机构
[1] Qatar Univ, Coll Pharm, Dept Pharmaceut Sci, QU Hlth, POB 2713, Doha, Qatar
[2] Univ Sains Malaysia, Sch Chem Sci, George Town 11800, Malaysia
[3] King Saud Univ, Coll Pharm, Dept Pharmacol & Toxicol, POB 2455, Riyadh 11451, Saudi Arabia
[4] Hamad Med Corp, Dept Surg, Surg Res Sect, Doha, Qatar
[5] Qatar Univ, Coll Hlth Sci, Dept Biomed Sci, QU Hlth, POB 2713, Doha, Qatar
[6] Jordan Univ Sci & Technol, Dept Chem Engn, POB 3030, Irbid 22110, Jordan
[7] Qatar Univ, Coll Arts & Sci, Dept Chem & Earth Sci, POB 2713, Doha, Qatar
[8] King Faisal Univ, Coll Med, POB 400, Al Hasa, Saudi Arabia
[9] Qatar Univ, Off Vice President Res & Grad Studies, Doha, Qatar
[10] Shanghai Jiao Tong Univ, Coll Life Sci & Biotechnol, Dept Bioinformat & Biostat, Shanghai 200240, Peoples R China
来源
CHEMISTRYSELECT | 2024年 / 9卷 / 37期
关键词
Binding free energy; Curcumin; Diabetes; Molecular docking simulation; Network pharmacology; AMBER;
D O I
10.1002/slct.202402379
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Diabetes mellitus is a chronic metabolic disorder affecting millions of people worldwide and causes serious complications such as diabetic nephropathy. Curcumin, a natural polyphenol derived from turmeric, has demonstrated antidiabetic, anti-inflammatory, and antioxidant properties. However, the molecular mechanisms underlying curcumin's anti-diabetic effects remain incompletely understood. This study employed network pharmacology, molecular docking, and simulation techniques to explore the potential targets, and key pathways of curcumin in the treatment of diabetes. Using SwissTarget prediction and Superpred databases, we predicted the molecular targets for curcumin, while diabetes-associated genes were obtained from DisGeNet. We identified 60 common targets for curcumin in diabetes. Protein-protein interaction (PPI) analysis revealed three sub-networks and ten hub genes with AKT1, TNF-alpha, EGFR, and STAT3 identified as key hub genes that could serve as potential biomarkers. Gene enrichment analysis indicated that these genes primarily regulate insulin resistance and other metabolic pathways. Quantum-polarized ligand docking (QPLD) showed that curcumin establishes multiple hydrogen and hydrophobic interactions with the essential amino acids of these hub targets. Molecular simulation results demonstrated stable dynamic behavior, a compact structure, and variations in residue flexibility. Binding free energy calculations using MM/GBSA and MM/PBSA methods validate curcumin's strong binding to the potential targets. Total binding free energy using MM/GBSA ranged from -21.35 to -30.94 kcal/mol while MM/PBSA calculations showed total binding free energy values between -19.80 and -26.66 kcal/mol. Altogether, this study provides valuable insights into the molecular targets of curcumin in diabetes and lays the foundation for future advancements in diabetes treatment. This study investigates curcumin's anti-diabetic effects using network pharmacology, molecular docking, and simulations. Among the key targets the interactions profiles were explored and the mechanisms were revealed through dynamic study. The study provides a new paradigm for discovering effective treatment for Diabetes. image
引用
收藏
页数:12
相关论文
共 59 条
  • [1] Tumor Necrosis Factor-Alpha: Role in Development of Insulin Resistance and Pathogenesis of Type 2 Diabetes Mellitus
    Akash, Muhammad Sajid Hamid
    Rehman, Kanwal
    Liaqat, Aamira
    [J]. JOURNAL OF CELLULAR BIOCHEMISTRY, 2018, 119 (01) : 105 - 110
  • [2] Arokiasamy P., 2021, Handbook of Global Health, P495
  • [3] Oxidative stress in the pathophysiology of type 2 diabetes and related complications: Current therapeutics strategies and future perspectives
    Bhatti, Jasvinder Singh
    Sehrawat, Abhishek
    Mishra, Jayapriya
    Sidhu, Inderpal Singh
    Navik, Umashanker
    Khullar, Naina
    Kumar, Shashank
    Bhatti, Gurjit Kaur
    Reddy, P. Hemachandra
    [J]. FREE RADICAL BIOLOGY AND MEDICINE, 2022, 184 : 114 - 134
  • [4] RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy
    Burley, Stephen K.
    Berman, Helen M.
    Bhikadiya, Charmi
    Bi, Chunxiao
    Chen, Li
    Di Costanzo, Luigi
    Christie, Cole
    Dalenberg, Ken
    Duarte, Jose M.
    Dutta, Shuchismita
    Feng, Zukang
    Ghosh, Sutapa
    Goodsell, David S.
    Green, Rachel K.
    Guranovic, Vladimir
    Guzenko, Dmytro
    Hudson, Brian P.
    Kalro, Tara
    Liang, Yuhe
    Lowe, Robert
    Namkoong, Harry
    Peisach, Ezra
    Periskova, Irina
    Prlic, Andreas
    Randle, Chris
    Rose, Alexander
    Rose, Peter
    Sala, Raul
    Sekharan, Monica
    Shao, Chenghua
    Tan, Lihua
    Tao, Yi-Ping
    Valasatava, Yana
    Voigt, Maria
    Westbrook, John
    Woo, Jesse
    Yang, Huanwang
    Young, Jasmine
    Zhuravleva, Marina
    Zardecki, Christine
    [J]. NUCLEIC ACIDS RESEARCH, 2019, 47 (D1) : D464 - D474
  • [5] Targeting the PI3K/Akt signaling pathway in pancreatic β-cells to enhance their survival and function: An emerging therapeutic strategy for type 1 diabetes
    Camaya, Inah
    Donnelly, Sheila
    O'Brien, Bronwyn
    [J]. JOURNAL OF DIABETES, 2022, 14 (04) : 247 - 260
  • [6] The Amber biomolecular simulation programs
    Case, DA
    Cheatham, TE
    Darden, T
    Gohlke, H
    Luo, R
    Merz, KM
    Onufriev, A
    Simmerling, C
    Wang, B
    Woods, RJ
    [J]. JOURNAL OF COMPUTATIONAL CHEMISTRY, 2005, 26 (16) : 1668 - 1688
  • [7] Chandran U, 2017, INNOVATIVE APPROACHES IN DRUG DISCOVERY: ETHNOPHARMACOLOGY, SYSTEMS BIOLOGY, AND HOLISTIC TARGETING, P127, DOI 10.1016/B978-0-12-801814-9.00005-2
  • [8] Chattopadhyay I, 2004, CURR SCI INDIA, V87, P44
  • [9] Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking
    Chen, Fu
    Liu, Hui
    Sun, Huiyong
    Pan, Peichen
    Li, Youyong
    Li, Dan
    Hou, Tingjun
    [J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2016, 18 (32) : 22129 - 22139
  • [10] cytoHubba: identifying hub objects and sub-networks from complex interactome
    Chin, Chia-Hao
    Chen, Shu-Hwa
    Wu, Hsin-Hung
    Ho, Chin-Wen
    Ko, Ming-Tat
    Lin, Chung-Yen
    [J]. BMC SYSTEMS BIOLOGY, 2014, 8