共 50 条
Exploring the anti-gout potential of sunflower receptacles alkaloids: A computational and pharmacological analysis
被引:5
|作者:
Wang K.
[1
]
Cui H.
[1
]
Liu K.
[2
]
He Q.
[2
]
Fu X.
[1
]
Li W.
[1
]
Han W.
[2
]
机构:
[1] Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun
[2] Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun
关键词:
Clustering algorithm;
LC-MS;
Molecular dynamics simulation;
Sunflower receptacles alkaloids;
Xanthine oxidase;
D O I:
10.1016/j.compbiomed.2024.108252
中图分类号:
学科分类号:
摘要:
Gout, a painful condition marked by elevated uric acid levels often linked to the diet's high purine and alcohol content, finds a potential treatment target in xanthine oxidase (XO), a crucial enzyme for uric acid production. This study explores the therapeutic properties of alkaloids extracted from sunflower (Helianthus annuus L.) receptacles against gout. By leveraging computational chemistry and introducing a novel R-based clustering algorithm, “TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)," we assessed 231 alkaloid molecules from sunflower receptacles. Our clustering analysis pinpointed six alkaloids with significant gout-targeting potential, particularly emphasizing the fifth cluster's XO inhibition capabilities. Through molecular docking and the BatchDTA prediction model, we identified three top compounds—2-naphthylalanine, medroxalol, and fenspiride—with the highest XO affinity. Further molecular dynamics simulations assessed their enzyme active site interactions and binding free energies, employing MM-PBSA calculations. This investigation not only highlights the discovery of promising compounds within sunflower receptacle alkaloids via LC-MS but also introduces medroxalol as a novel gout treatment candidate, showcasing the synergy of computational techniques and LC-MS in drug discovery. © 2024 Elsevier Ltd
引用
收藏
相关论文