Designing of Insecticides against Bemisia tabaci using computational approaches
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Date
2022
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Department of Genetics and Plant Breeding Institute of Agricultural Sciences Banaras Hindu University Varanasi
Abstract
Bemisia tabaci is a major destructive pest that destroys more than 600 crop species worldwide. Its feeding behaviour on phloem sap and secretion of honeydew on leaves leads to the accumulation of mold that impairs photosynthesis and fruit quality. Additionally, it is responsible for transferring more than 100 viruses in plants which interferes with plant growth by becoming a limiting growth factor. This pest is responsible for a huge number of economic losses. This research aims to find novel natural compounds from available databases using molecular docking (MD), and molecular dynamics simulation (MDS) approaches. In this work, I have targeted the ecdysone receptor of Bemisia tabaci as it is involved in metamorphosis, cell differentiation and reproduction processes. No similar receptor is present in mammals which makes it an ideal target. The sequence of EcR has retrieved from the NCBI database and then BLAST (Sayers et al., 2021) against the UniProt database (The UniProt Consortium et al., 2021). Multiple sequence alignment by MUSCLE web-server (Edgar, 2004) has been performed. Further BLAST against PDB database to find out available 3D structures. Only one ligand-binding domain structure was available; hence I have modelled the full-length protein using alphafold 2.2.0 (Jumper et al., 2021). I have checked for the disordered regions of the protein by using two IDP predictor software, i.e. DEPICTER (Barik et al., 2020) and PrDOS (Ishida & Kinoshita, 2007). Further, molecular dynamics simulation was performed to equilibrate the best model predicted by alphafold in an aqueous and ionic environment to check it’s the stability and flexibility. From the simulation, it has been found out though the IDR part is highly flexible, but the ligand-binding domain is very stable and compact. Therefore, I considered the last conformation from the simulation for further studies. Next, 32,552 bacterial and fungal secondary metabolites were retrieved from the npatlas 2.0 database (van Santen et al., 2022) and docked each metabolite with the simulation obtained last conformation of EcR protein using idock 2.2.3 software (Li et al., 2012). I have chosen cut-off -11kcal/mol binding energy and found 37 metabolites. I have redock these 37 metabolites again with another well- known docking software, i.e. Autodock vina 1.1.2 (Trott & Olson, 2009), 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. The top five consistent compounds were selected, and protein-ligand interactions were studied using ligplots (Wallace et al., 1995). Finally, I concluded these 37 molecules might be possible hits for EcR protein, and the top five selected compounds are needed priority for experimental validations.
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Bemisia tabaci