Reviews & Perspectives
Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling
In Brief PL has emerged as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and protein–protein interaction networks in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte–neuron junctions. Here, we review recent advances in PL methods and their application to neurobiology.Are There Indeed Spliced Peptides in the Immunopeptidome?
In Brief Peptide splicing was suggested to significantly contribute ligands to the immunopeptidome. This article argues that peptide splicing is at most very rare, even if it happens at all. Considerations against peptide splicing are based on bioinformatics calculations related to the analysis of the LC-MS/MS data, and on the abundance of water in the cells, which should compete effectively with the transpeptidation reaction, needed for peptide splicing.Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis
In Brief This article provides a systematic review of the most recent MS-based strategies and corresponding software tools for the analysis of intact glycopeptides, particularly intact N-glycopeptides, reported in the last decade, including the process of identifying N-glycopeptides from MS data, the existing methods of MS data acquisition and interpretation, the quality control methods, the display of results, and the software applications.The Role of Data-Independent Acquisition for Glycoproteomics
In Brief As a highly abundant and diverse post-translational modification, protein glycosylation is challenging to characterize in various approaches including MS. In MS-based proteomics, data-independent acquisition (DIA) has been advanced rapidly and showed outstanding analytical performances. DIA now started to be applied in different facets of glycoproteomics, including deglycosylated and intact N-linked and O-linked glycopeptides, and screening of oxonium ions. We summarized current applications of DIA in glycoproteomics and discussed its limitations and perspectives.Calculating Glycoprotein Similarities From Mass Spectrometric Data
In Brief To understand the roles of glycoproteins in biological processes, it is necessary to quantify the changes that occur to glycosylation at individual sites and to the whole molecule. That glycoprotein glycosylation is inherently heterogeneous means that the distribution of glycoforms at each glycosite must be quantified in order to inform calculation of molecular similarities. We review analytical and statistical methods for determining glycoprotein molecular similarities from glycoproteomics data.
