Designing of Insecticides Against Bemisia tabaci targeting ecdysone receptor
Bemisia tabaci is a major destructive pest that destroys more than 600 crop species worldwide. It is also responsible for transferring more than 100 viruses in plants which interferes with plant growth by becoming a limiting growth factor. This project aims to find novel lead molecules using computational approaches. The ecdysone receptor of B tabaci is involved in metamorphosis, cell differentiation and reproduction processes. No similar protein is present in mammals which makes it an ideal target. The unavailability of a full-length structure in PDB lead us to model the full-length protein using Alphafold 2.2.0 1. The disordered regions of the protein were predicted by using IDP predictor software, i.e. DEPICTER 2. Further, 32,552 bacterial and fungal secondary metabolites were retrieved from the npatlas 2.0 database 3 and docked each metabolite with the simulation obtained last conformation of EcR protein using idock 2.2.3 software 4. I have chosen a cut-off -10 kcal/mol binding energy and found 14 metabolites. I have redock these 14 metabolites again with Autodock vina 1.1.2 5 to validate idock 2.2.3 results and found an almost similar result with minor deviations. These dockings were compared with 20E, a natural hormone binding with EcR protein. Lastly, one compound K6323 with the most suitable scoring function were selected for 30 ns MD simulations of the protein complex with E20 and K6323 and compared with apo form of the protein. Further, QMMM/GBSA-based binding energy was calculated from 100 snapshots from MD simulation. The binding energy of K6323 was found to be better than the natural inhibitor 20E. These computational predictions can be analysed further experimentally.
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