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

Abstract

Backgrounds: 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.

Summary: Recently, an approach combining machine learning-based prediction of novel peptide-GPCR pairs and experimental validation of the predicted pairs have been shown to breakthrough this bottleneck. A machine learning method, logistic regression for human class A GPCRs and the multiple subsequent signaling assays led to the deorphanization of human class A orphan GPCRs, namely, the identification of 18 peptide-GPCR pairs. Furthermore, using another machine learning algorithm, the support vector machine (SVM), the peptide descriptor-incorporated SVM was originally developed and employed to predict GPCRs for novel peptides characterized from the closest relative of vertebrates, Ciona intestinalis Type A (Ciona robusta). Experimental validation of the predicted pairs eventually led to the identification of 11 novel peptide-GPCR pairs. Of particular interest is that these newly identified GPCRs displayed neither significant sequence similarity nor molecular phylogenetic relatedness to known GPCRs for peptides.

Key messages: These recent studies highlight the usefulness and versatility of machine learning for enabling the efficient, reliable, and systematic identification of novel peptide-GPCR interactions.

Keywords: Deorphanization; G protein-coupled receptor; Machine learning; Neuropeptide; Peptide hormone.