International Journal of Pharmaceutical and Phytopharmacological Research
ISSN (Print): 2250-1029
ISSN (Online): 2249-6084
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2023   Volume 13   Issue 1

Computational Analysis to Study the Insecticidal Properties of Lectin Protein Through Docking Studies
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Chandra R, Yadav S. Computational Analysis to Study the Insecticidal Properties of Lectin Protein Through Docking Studies. Int J Pharm Phytopharmacol Res. 2023;13(1):1-6. https://doi.org/10.51847/HEsPGhBGOP
APA
Chandra, R., & Yadav, S. (2023). Computational Analysis to Study the Insecticidal Properties of Lectin Protein Through Docking Studies. International Journal of Pharmaceutical And Phytopharmacological Research, 13(1), 1-6. https://doi.org/10.51847/HEsPGhBGOP
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Abstract

Lectins belong to one of those protein families that exist in nature in abundance. Out of these, legume lectins are widely spread and display interesting antimicrobial and insecticidal properties. This is because, they recognize and bind to specific carbohydrate components without making any changes in the covalent assembly of recognized ligands, which makes them a suitable target candidate for useful applications in food and agriculture Moreover, numerous studies have demonstrated that the lectins found in legumes are poisonous to hemipterans. This study aims to elaborate on the insecticidal properties of Cajanus cajan lectin (CCL) with receptor alanyl Aminopeptidase N (APN) from Acyrthosiphon pisum membrane and other insect origins through molecular modeling. The Protparam tool elaborated on the usefulness of physico-chemical parameters such as the aliphatic index, theoretical isoelectric point, and instability index along with the functional domains of the protein. The secondary structure of the CCL and APN was predicted to explain their structural and behavioral properties. Molecular docking was done between CCL as ligand and APN as receptor from different insect origins to obtain the cluster scores. To validate the current knowledge of the CCL protein structure and to demonstrate the prospective candidate gene for developing transgenics for improved aphid or other insect resistance, cluster scores with the lowest energy were evaluated using a variety of bio-computational tools.

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