Designing of Insecticides Against Bemisia tabaci targeting
ecdysone receptor

., Indu, Jozef HRITZ, Václav BRÁZDA, Rajesh KUMAR and Krishnendu BERA. Designing of Insecticides Against Bemisia tabaci targeting ecdysone receptor. In BERA, Krishnendu. 6th Advanced in Silico Drug Design workshop/challenge, 30st January – 3rd February 2023, Faculty of Science, Palacky University, Olomouc, Czech Republic. 2023.

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TY  - CONF
ID  - 2252558
AU  - ., Indu - Hritz, Jozef - Brázda, Václav - Kumar, Rajesh - Bera, Krishnendu - Bera, Krishnendu
PY  - 2023
TI  - Designing of Insecticides Against Bemisia tabaci targeting ecdysone receptor
UR  - https://www.kfc.upol.cz/wp-content/uploads/2023/01/6ADD_BoA.pdf
N2  - 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.

References:

1. Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995) Computer Physics Communications.

2. Carmichael, J. A., et al. (2005). J. of Biological Chem., 280(23), 22258–22269.

3. Barro D. et al. (2011). Annual Review of Entomology, 56(1), 1–19.

ER -

Basic information
Original name	Designing of Insecticides Against Bemisia tabaci targeting ecdysone receptor
Authors	., Indu, Jozef HRITZ, Václav BRÁZDA, Rajesh KUMAR and Krishnendu BERA.
Edition	6th Advanced in Silico Drug Design workshop/challenge, 30st January – 3rd February 2023, Faculty of Science, Palacky University, Olomouc, Czech Republic, 2023.

Other information
Type of outcome	Conference abstract
Confidentiality degree	is not subject to a state or trade secret
WWW	Abstract Book
Changed by	Changed by: Krishnendu Bera, Ph.D., učo 490519. Changed: 4/2/2023 11:04.

Abstract

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.

References:

1. Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995) Computer Physics Communications.

2. Carmichael, J. A., et al. (2005). J. of Biological Chem., 280(23), 22258–22269.

3. Barro D. et al. (2011). Annual Review of Entomology, 56(1), 1–19.

Links
GF20-05789L, research and development project	Name: Charakterizace přirozeně neuspořádaných proteinů
GF20-05789L, research and development project	Investor: Czech Science Foundation
LM2018140, research and development project	Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)
LM2018140, research and development project	Investor: Ministry of Education, Youth and Sports of the CR
MUNI/G/1002/2021, interní kód MU	Name: Insight into CAIX structure and function and design of selective inhibitors as potential anti-cancer drugs (Acronym: CAIX-target)
MUNI/G/1002/2021, interní kód MU	Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects

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Designing of Insecticides Against Bemisia tabaci targeting ecdysone receptor

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