Open AccessImpact of Machine Learning-Associated Research Strategies on the Identification of Peptide-Receptor Interactions in the Post-Omics Era
Author(s) -
Honoo Satake,
Tomohiro Osugi,
Akira Shiraishi
Publication year - 2021
Publication title -
neuroendocrinology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.493
H-Index - 101
eISSN - 1423-0194
pISSN - 0028-3835
DOI - 10.1159/000518572
Subject(s) - g protein coupled receptor , computational biology , ciona , biology , identification (biology) , artificial intelligence , machine learning , bioinformatics , support vector machine , peptide , computer science , receptor , ciona intestinalis , biochemistry , gene , botany
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.