S-acylation, often referred to as S-palmitoylation, is a reversible and dynamic posttranslational modification that corresponds to the addition of a long-chain fatty acid to cysteine (Cys) residues. Established mass spectrometry-based chemoproteomics methods have improved our understanding of the S-acylation proteome, notably by identifying hundreds of S-acylated proteins, sometimes with the modified Cys. However, the precise quantification of S-acylation levels for each Cys within a single sample remains challenging at the proteome level. Quantification of S-acylation levels is critical to further our understanding of protein S-acylation in cellular function and its role in health and diseases. We report here the development of an S-acylation quantification workflow based on the sequential labeling of free Cys and S-acylated Cys with isotopic labeling reagents. The workflow was extensively optimized, notably by comparing the number of sites identified with two alkyne-tagged Cys-reactive isotopic probes and four azido-tagged biotin-based capture reagents. By integrating this enhanced workflow with high-field asymmetric waveform ion mobility spectrometry (FAIMS) on LC–MS/MS instruments for the separation of labeled peptides, over 17,000 unique Cys could be quantified in biological samples. Application of the S-acylation quantification workflow to cellular proteomes allowed for the quantification of S-acylation levels in a HeLa proteome. We also identified dynamic S-acylation changes in response to autophagy induction.

Koina is an open-source, online platform that simplifies access to machine learning models in proteomics, enabling easier integration into analysis tools and helping researchers adopt and reuse ML mod...

Covalent inhibitors are powerful entities in drug discovery. Now the amino acid selectivity and reactivity of a diverse electrophile library have been assessed proteome-wide using an unbiased workflow...

Photo-crosslinking mass spectrometry enables the identification of protein-RNA interactions in living cells, pinpointing interaction interfaces at single-amino acid resolution. However, current isolat...

Our understanding of how post-translational modification—protein phosphorylation—impacts the complexity of eukaryotic signalling pathways is continuously expanding. Now, protein oligophosphorylation h...

FragPipe is recognized as one of the fastest computational platforms in proteomics, making it a practical solution for the rapid quality control of high-throughput sample analyses. Starting with version 23.0, FragPipe introduced the “Generate Summary Report” feature, offering .pdf reports with essential quality control metrics to address the challenge of intuitively assessing large-scale proteomics data. While traditional spreadsheet formats (e.g., tsv files) are accessible, the complexity of the data often limits user-friendly interpretation. To further enhance accessibility, PSManalyst, a Shiny-based R application, was developed to process FragPipe output files (psm.tsv, protein.tsv, and combined_protein.tsv) and provide interactive, code-free data visualization. Users can filter peptide-spectrum matches (PSMs) by quality scores, visualize protease cleavage fingerprints as heatmaps and SeqLogos, and access a range of quality control metrics and representations such as peptide length distributions, ion densities, mass errors, and wordclouds for overrepresented peptides. The tool facilitates seamless switching between PSM and protein data visualization, offering insights into protein abundance discrepancies, samplewise similarity metrics, protein coverage, and contaminants evaluation. PSManalyst leverages several R libraries (lsa, vegan, ggfortify, ggseqlogo, wordcloud2, tidyverse, ggpointdensity, and plotly) and runs on Windows, MacOS, and Linux, requiring only a local R setup and an IDE. The app is available at (https://github.com/41ison/PSManalyst.

Targeted covalent inhibitors are powerful entities in drug discovery, but their application has so far mainly been limited to addressing cysteine residues. The development of cysteine-directed covalen...

LONP1, whose expression is downregulated in islets from donors with type 2 diabetes, is vital to mediate efficient mitochondrial protein folding, thus preventing proteotoxicity and promoting islet β c...

Comparison of DIA to spectral counting and TMT quantitative techniques using animal lens studies - pwilmart/quantitative_proteomics_comparison

Targeted proteomics technologies, and specially data-independent acquisition techniques, have revolutionized the landscape of proteomic research in the last decade offering researchers unprecedented …

Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful technique for detecting protein structural changes and drug-prot…

Proteomics often misses co-fragmented peptides in DDA data. Here, the authors introduce MSFragger-DDA+, a database search tool that enhances peptide identification by searching the full isolation wind...

A cross-platform proteomics data analysis suite. Contribute to Nesvilab/FragPipe development by creating an account on GitHub.

SCF–FBXO31 scans proteins for C-terminal amidation and marks them for subsequent proteasomal degradation.

Data-independent acquisition advances proteomics quantification. Here, the authors present diaTracer, a spectrum-centric tool for diaPASEF data that supports broad proteomics applications, enabling di...

Targeted covalent inhibitors are powerful entities in drug discovery, but their application has so far mainly been limited to addressing cysteine residues. The development of cysteine-directed covalen...