Sudhir KumarSharma, Drishta2016-10-272016-10-272012http://krishikosh.egranth.ac.in/handle/1/82367Peroxisome proliferator-activated receptors are ligand activated transcription factors that belong to the nuclear receptor super family and are involved in control of energy, lipid and glucose homeostasis. There are three different isotypes of the receptor namely, PPARα, PPARβ/δ and PPARγ. The moderate (or partial) levels of PPAR-γ activation coordinate an evolutionary beneficial and adaptive response. Current evidence shows that there are natural PPAR activators, which brings this tightly regulated system out of balance, and contributes to the pathogenesis of life-style associated diseases, such as obesity, type2 diabetes, and atherosclerosis. This also indicates that inhibiting PPAR activity, rather than activating, might be the preferred therapeutic strategy to treat the above disorders. In this study, virtual high throughput screening of PPAR inhibitors was performed using UCSF DOCK 6.0. The three sets of databases were prepared namely, NCI database, ZIN C (target-wise) database and ZINC (lead-like) database. PPARγ was selected as the receptor as the three isotypes were 80% similar. The ouput of the first run (NCI database) contained 1516 compounds. The output of second run (ZINC target-wise) contained 20 compounds. The output of third run (ZINC lead-like) contained 103 compounds. Scoring was done for each compound by summing the energies calculated by DOCK. The cluster analysis was done using Ches-Mapper. In the first run, NCI dataset was used, in which 1516 compounds were taken and resulted in the identification of 8 ligands. Out of 20 compounds obtained from virtual screening of ZINC (target-wise), two of them were identified as the ligands. In the third run, ZINC (lead-like) database was used in which 7 ligands were obtained. Lastly, the obtained hits were compared with the known inhibitors and it was found that the groups present in the inhibitors are similar to the groups present in the hits.enLigands, Zinc, Biological phenomena, Selection, Organic compounds, Biochemical compounds, Acidity, Proteins, Diseases, TranscriptionThesis