Abstract
Obesity, characterized by abnormal or excessive fat accumulation, has become a chronic degenerative health condition that poses significant threats to overall well-being. Pharmacological intervention stands at the forefront of strategies to combat this issue. Recent studies, notably by Umut Ozcan's team, have uncovered the remarkable potential of Celastrol, a small-molecule compound derived from the traditional Chinese herb thunder god vine (Tripterygium wilfordii) as an anti-obesity agent. In this research, computational chemical analysis was employed, incorporating the "TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)" algorithm to systematically explore 139 active small molecules from thunder god vine. These compounds were classified into six categories, with a particular focus on Category 1 molecules for their exceptional binding affinity to obesity-related targets, offering new avenues for therapeutic development. Using advanced molecular docking techniques and Cytoscape prediction models, six representative Celastrol-like molecules were identified, namely 3-Epikatonic Acid, Hederagenin, Triptonide, Triptotriterpenic Acid B, Triptotriterpenic Acid C, and Ursolic Acid. These compounds demonstrated superior binding affinity and specificity toward two key obesity targets, PPARG and PTGS2, suggesting their potential to regulate fat metabolism and mitigate inflammatory responses. To further substantiate these findings, molecular dynamics simulations and MM-PBSA free-energy calculations were applied to analyze the dynamic interactions between these small molecules and the enzymatic active sites of their targets. The results provide robust theoretical evidence that support the feasibility of these molecules as promising candidates for anti-obesity therapies. This study underscores the power of the 3DHFC-TRS algorithm in uncovering bioactive compounds from natural sources, such as thunder god vine, and highlights the therapeutic promise of PPARG and PTGS2 as novel obesity-related targets. Furthermore, it emphasizes the essential role of computational science in expediting drug discovery, paving the way for personalized and precision-based treatments for obesity and heralding a future of more effective healthcare solutions.