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

Authors :
Satake, Honoo
Osugi, Tomohiro
Shiraishi, Akira
Source :
Neuroendocrinology; 2023, Vol. 113 Issue 2, p251-261, 11p
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00283835
Volume :
113
Issue :
2
Database :
Complementary Index
Journal :
Neuroendocrinology
Publication Type :
Academic Journal
Accession number :
161937191
Full Text :
https://doi.org/10.1159/000518572