Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS2 spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.

Lipid droplets (LDs) are multifunctional organelles consisting of a central compartment of non-polar lipids shielded from the cytoplasm by a phospholipid monolayer. The excessive accumulation of LDs in cells is closely related to the development and progression of many diseases in humans and animals, such as liver-related and cardiovascular diseases. Thus, regulating the LDs size and abundance is necessary to maintain metabolic homeostasis. This study found that lipopolysaccharide (LPS) stimulation reduced the LDs content in the mouse liver. We tried to explain the possible molecular mechanisms at the broad protein and mRNA levels, finding that inhibition of the peroxisome proliferator-activated receptors (PPAR) signalling pathway by LPS may be a critical factor in reducing LDs content.

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Lysine 2-hydroxyisobutyrylation (Khib) is a novel protein post-translational modifications (PTMs) observed in both eukaryotes and prokaryotes. Recent studies suggested that this novel PTM has the potential to regulate different proteins in various pathways. Khib is regulated by lysine acyltransferases and deacylases. This novel PTM reveals interesting connections between modifications and protein physiological functions, including gene transcription, glycolysis and cell growth, enzymic activity, sperm motility, and aging. Here, we review the discovery and the current understanding of this PTM. Then, we outline the networks of complexity of interactions among PTMs in plants, and raise possible directions of this novel PTM for future investigations in plants.

Sulfated N- and O-glycans exist in trace levels which are challenging to detect, especially when abundant neutral and sialylated glycans are present. Current matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS)-based sulfoglycomics approaches effectively utilize permethylation to discriminate sulfated glycans from sialyl-glycans. And a charge-based separation to isolate the sulfated glycans from the rest of the permethylated neutral and sialyl-glycans. However, these approaches suffer from concomitant sample losses during cleanup steps. Herein, we describe Glycoblotting as a straightforward complementary method with seamless glycan purification, enrichment, methylation, and labeling on a single platform to address sulfated glycan enrichment, sialic acid methylation, and sample loss. Glycoblottings' on-bead chemoselective ligation of reducing sugars with hydrazide showed excellent recovery of sulfated glycans, allowing the detection of more sulfated glycan species. On-bead methyl esterification of sialic acid using 3-methyl-1-p-tolyltriazene (MTT) effectively discriminates sulfated glycans from sialyl-glycans. Furthermore, we have shown that using MTT as a methylating agent allowed us to simultaneously detect and differentiate sulfate from phosphate groups in isobaric N-glycan species. We believe that Glycoblotting will contribute significantly to the MALDI-TOF MS-based Sulphoglycomics workflow.

Native mass spectrometry is a rapidly emerging technique for fast and sensitive structural analysis of protein constructs, maintaining the protein higher order structure. The coupling with electromigration separation techniques under native conditions enables the characterization of proteoforms and highly complex protein mixtures. In this review, we present an overview of current native CE-MS technology. First, the status of native separation conditions is described for capillary zone electrophoresis (CZE), affinity capillary electrophoresis (ACE), and capillary isoelectric focusing (CIEF), as well as their chip-based formats, including essential parameters such as electrolyte composition and capillary coatings. Further, conditions required for native ESI-MS of (large) protein constructs, including instrumental parameters of QTOF and Orbitrap systems, as well as requirements for native CE-MS interfacing are presented. On this basis, methods and applications of the different modes of native CE-MS are summarized and discussed in the context of biological, medical, and biopharmaceutical questions. Finally, key achievements are highlighted and concluded, while remaining challenges are pointed out.