In silico modeling of structural interactions between bioactive peptides and GPCRs for identifying agonists and antagonists in novel arthropod pest management strategies

The advancement of novel and sustainable strategies that focus on the long-term prevention of pests or the damage caused by them is currently a zealous area of research. One promising approach in pest management specifically targets insect neuropeptides (NPs) and their G protein-coupled receptors (GPCRs), which regulate essential physiological and behavioral processes throughout insect life stages. These components serve as excellent biological targets for the development of next-generation pesticides. In the current study, we targeted the insect CAPA (capability) neuropeptide system, focusing CAPA GPCRs from two economically important pest species: spotted-wing drosophila (Drosophila suzukii) and western flower thrips (Frankliniella occidentalis). Homology modelling and AlphaFold structure prediction methods were utilized for the prediction of the 3D structure of these GPCRs and peptides. AlphaFold structure prediction method which uses deep learning to predict the structure of proteins directly from the amino acid sequences surpassed homology modeling approaches that rely heavily on the availability of suitable templates in both accuracy and quality. Using molecular docking, we examined the interactions between CAPA GPCRs and insects PRXamide peptides: CAPA, pyrokinin (PK), and ecdysis-triggering hormone (ETH). The results revealed divergence in the nature of receptor-ligand interactions across these peptides subfamilies, highlighting the specificity and variability in receptor recognitions. This could be used to predict the orthosteric pocket in the GPCR which can then be targeted to screen various available peptide libraries to develop agonists and antagonists. Additionally, this computational-based bioactive peptide screening approach offers reduced costs and faster turnaround times.