Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications

• Main Authors: Mohammed Albratty (Author), Neelaveni Thangavel (Author), Balakumar Chandrasekaran (Author), Abdulkarim M. Meraya (Author), Hassan Ahmad Alhazmi (Author), Sankar Muthumanickam (Author), Pandi Boomi (Author), Natarajan Boopala Bhagavan (Author), Safaa F. Saleh (Author)
• Format: Book
• Published: Pensoft Publishers, 2024-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.3897/pharmacia.71.e118949; 2603-557X
• Keywords: Pharmacy and materia medica; RS1-441
• Source: Pharmacia, Vol 71, Iss , Pp 1-10 (2024)

Inhibition of aldose reductase (AR) could be a beneficial strategy for managing diabetes-associated complications. Trigonella foenum-graecum (TFG) is used around the globe as a traditional medicine for the management of diabetes. Our study aimed to assess the potential of TFG phytocompounds as inhibitors of AR in the context of diabetes-related complications. Our research work employed molecular docking, density functional theory (DFT) and molecular dynamics (MD) to evaluate the efficacy of TFG compounds. The study compared the predictive power of AutoDock and AutoDock Vina docking software and found that AutoDock Vina performs better in ranking and discriminating actives and decoys. The research identified five compounds as potential AR inhibitors from fifty-eight reported TFG phytoconstituents. Tigogenin and Gitogenin stood out as the most promising AR inhibitors. The electronic properties of the compounds were analysed through DFT studies and provided insights into their binding potential. Finally, the results of MD simulations indicated that Tigogenin and Gitogenin bound robustly with AR throughout the simulation period. This study predicted the AR inhibitory potential of TFG compounds for managing diabetes-associated complications and supports further drug development from TFG. The benchmarking approach used in this study improves the accuracy and dependability of bioactivity prediction.