Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs - Yamane, Haruki; Ishida, Takashi (Frontiers in Bioinformatics, 2023)
Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs and are important for drug discovery. Various computational approaches have been applied to predict their ligands. Among them, the compound-protein interaction (CPI) prediction has been considered one of the most suitable for class A GPCRs due to a large number of orphan receptors. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model employs the whole protein sequence as the input. However, only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family.