Cell type specificity of signaling: view from membrane receptors distribution and their downstream transduction networks

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

[Biased Signaling through G Protein-coupled Receptors]

It is well-established that G protein-coupled receptors (GPCRs) transduce signals into cells using G proteins as intermediary molecules. β-Arrestins are molecules involved in regulating GPCRs; however, it has recently been reported that β-arrestins can also mediate signaling through GPCRs. Signaling through G proteins or β-arrestins can be activated selectively using specific agonists; of the latter, those that can selectively activate either G proteins or β-arrestins are called biased agonists. The clinical use of biased agonists could potentially induce fewer side effects. However, partial agonists can also explain the mechanism of G protein-biased agonists; thus, appropriate assay systems must be considered. Endogenous agonists are known to bind to orthosteric and allosteric sites in the agonist binding site, and the allosteric site is associated with the activity of biased agonists. This current review presents a detailed discussion of biased agonists.

Pharmacology of Antagonism of GPCR

Agonists are defined as the ligands that activate intracellular signaling and evoke cellular responses. Synthetic and endogenous agonists should bind specific amino acids to activate G protein-coupled receptor (GPCR). Agonists that induce maximal responses are full agonists. Partial agonists cannot induce full responses unlike full agonists. In definition, antagonists inhibit agonist-stimulated responses by binding to orthosteric or allosteric sites. Antagonists modulate agonist-induced responses and are often related with inverse agonist activity. However, the relationship between antagonists and partial agonists is complex. An antagonist behaves as a partial agonist when the constitutive activity of the GPCR is high. In contrast, a partial agonist with very weak intrinsic activity may be classified as an antagonist. Thus, antagonisms of the compounds are influenced by constitutive activity of GPCRs, intrinsic activity and differences in the binding sites of GPCRs. Since "antagonism" has been revealed to have multiple aspects and more complex than previously thought, it may be difficult to classify each compound as simply "agonist" or "antagonist" as before. In this review, we discuss the recent findings and perspectives on the pharmacology of GPCR-binding antagonists, inverse agonists, and signaling.