Impact of Machine Learning-Associated Research Strategies on the Identification of Peptide-Receptor Interactions in the Post-Omics Era

Elucidation of peptide-receptor pairs is a prerequisite for many studies in the neuroendocrine, endocrine, and neuroscience fields. Recent omics analyses have provided vast amounts of peptide and G protein-coupled receptor (GPCR) sequence data. GPCRs for homologous peptides are easily characterized based on homology searching, and the relevant peptide-GPCR interactions are also detected by typical signaling assays. In contrast, conventional evaluation or prediction methods, including high-throughput reverse-pharmacological assays and tertiary structure-based computational analyses, are not useful for identifying interactions between novel and omics-derived peptides and GPCRs.