In silico identification of potential 2-thioxothiazolidin-4-one derivatives against peroxisome proliferator-activated receptor-gamma by molecular docking, MM-GBSA, molecular dynamic simulation, and toxicity studies
The metabolic disorder diabetes mellitus occurs when there is an inadequate level of insulin or excessive insulin resistance. There has been evidence that this pathogenesis has been approached <i>via</i> a different strategy. It activates homeostatic glucose and improves peripheral glucose utilization by targeting the PPAR-γ. In this regard, the present study aims to investigate 2-thioxothiazolidin-4-one derivatives <b>(D1-15)</b> as PPAR-γ regulators by computational approach followed by evaluation of physicochemical properties and ADMET profiles. The protein target PPAR-γ (PDB ID: 3DZY) was docked against 2-thioxothiazolidin-4-one derivatives using the glide suite. The results indicate the high binding affinity of compounds <b>D7, D11</b> (-6.509 and -7.276 kcal/mol) and they were compared against standard drug rosiglitazone <b>(-7.289</b> kcal/mol). MD simulation studies were also carried out at 150 ns to identify the key interactions in protein-ligand complexes in a dynamic environment not only to confirm our findings but to validate them as well. The findings indicated that certain designed compounds, namely D7 and D11, exhibited notable activity against PPAR-γ inhibitors, as evidenced by their favorable glide scores. The chosen derivatives of 2-thioxothiazolidin-4-one appear to hold promise as potential sources for advancing the development of antidiabetic agents targeting the PPAR-γ enzyme.
Authors
- Mahendra Gowdru Srinivasa
- Manohar Mahadev (ORCID: https://orcid.org/0000-0002-4041-1563)
- Sudeep D. Ghate (ORCID: https://orcid.org/0000-0001-9996-3605)
- Shivakiran Alva (ORCID: https://orcid.org/0000-0003-2027-2356)
- Vinay C. Sangamesh
- B. C. Revanasiddappa
Institutions
- Nitte University (IN)
Publication Details
- Journal
- Journal of Biomolecular Structure and Dynamics
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1080/07391102.2025.2506713
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01