Identification of novel antagonists of the ecdysone receptor from the desert locust (Schistocerca gregaria) by in silico modelling

The desert locust, Schistocerca gregaria Forsskål, is the most destructive migratory pest, which continually damages large areas of cropland and pastures in various parts of the world. Chemical insecticides are currently being used to control desert locusts. However, due to the harmful effects of conventional insecticides on human health and the environment, as well as the emergence of insecticides-resistant insects, alternative pest management programs must be developed. Given the critical role of the ecdysone receptor (EcR) in insect development, this study aimed to use computational tools to identify compounds with antagonistic properties against the desert locust EcR. Understanding the biochemical and structural properties of EcR is required for designing target-specific inhibitors, so we first used several bioinformatics tools to investigate the physicochemical properties, secondary structures, and topology of EcR from S. gregaria. SWISS-MODEL was used to predict the three-dimensional structural models of EcR, and the reliability of the predicted model was validated by various programs. Molecular docking studies between eight locust-derived protease inhibitors and the predicted model of EcR revealed the antagonistic capacity of all the studied inhibitors against EcR. However, the inhibitor 1KJ0 had the best docking score, the lowest binding energy and dissociation constant, and the greatest number of hydrogen bonds and non-bonded contacts with EcR, indicating its strong antagonistic potency against EcR. Our findings highlight the importance of computational studies in identifying novel antagonists to a target protein. However, in vitro and in vivo investigations are further required to validate the potency of the introduced compound.