Clathrin-dependent endocytosis plays a critical role in larval and pupal development, and female oocyte production in the red flour beetle (Tribolium castaneum)

Role of odorant binding protein C12 in the response of Tribolium castaneum to chemical agents

Tribolium castaneum is a worldwide pest of stored grain that mainly damages flour, and not only causes serious loss of flour quality but also leads to deterioration of flour quality. Chemical detection plays a key role in insect behavior, and the role of odorant-binding proteins (OBPs) in insect chemical detection has been widely studied. OBPs can interact with small molecule compounds and thereby modulate variation in insecticide susceptibility in insects. In this study, a total of 65 small molecule compounds are selected to investigate the bound effect with TcOBP C12. The molecular docking results showed that β-caryophyllene, (-)-catechin, butylated hydroxytoluene, diphenyl phthalate and quercetin were the top five compounds, with docking binding energies of -6.11, -5.25, -5.09, -5.05, and - 5.03 Kcal/mol, respectively. Molecular dynamics analysis indicated that odorant binding protein C12 (TcOBP C12) exhibited high binding affinity to all five tested chemical ligands, evidenced by fluorescence quenching assay in vitro. In addition, the contact toxicity assay results suggested that these chemical agents caused a dose-dependent increase in mortality rate for T. castaneum adults. The TcOBP C12 gene was upregulated >2 times after a 24-h exposure, indicating that OBP C12 may play an important role for T. castaneum in response to these chemical agents. In conclusion, our results provide a theoretical basis for future insecticide experiments and pest management.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.