Biology subjects

Meelker Gonzalez, R.

Publications and source records attributed to Meelker Gonzalez, R..

3 recordsLinked to original sources

bioRxiv2025

Decoding isozyme-specific substrate recognition in protein arginine deiminases by proteome-wide citrullination mapping

Protein arginine deiminases (PADs) convert arginine to citrulline, altering protein structure and function. Of the five human isozymes, PAD1-4 are catalytically active with distinct tissue-specificities, yet isozyme-specific substrate recognition remains poorly defined. We profiled PAD1-4 substrate landscapes via mass spectrometry, identifying [~]30,000 citrullination sites across [~]5,500 proteins. Only 14% of sites were shared among all four, reflecting distinct sequence preferences: PAD1-2 showed broad specificity, whereas PAD3-4 favored arginines flanked by acidic or glycine residues. These preferences persisted over 10 min-16 h, indicating sequence context rather than temporal dynamics drives specificity. Mutation analysis of eleven PAD4 variants revealed Q346, G403, R639, and H640 as key determinants distinguishing substrate recognition from that of PAD2. This work provides the most comprehensive PADs substrate atlas to date, defining isozyme-specific motifs and molecular determinants, and guiding development of selective inhibitors and probes to interrogate citrullination mechanisms in health and disease.

biochemistry↗

bioRxiv2025

High-throughput chemical proteomics workflow for profiling protein citrullination dynamics

Citrullination is a post-translational modification implicated in autoimmune and inflammatory diseases, yet its low abundance and lack of effective enrichment tools have limited proteome-wide analysis. Here, we developed a robust chemical proteomics workflow with improved specificity and throughput. This method builds upon glyoxal-based derivatization and incorporates a cleavable biotin linker for efficient peptide enrichment, release, and identification via mass spectrometry. Benchmarking across biological systems demonstrated a >10-fold increase in the detection of citrullinated peptides (> 150-fold increase in intensity) at sub-0.1% abundance. Applying the workflow to mouse brain tissue and human primary neutrophils revealed dynamics and condition-specific changes in the citrullinome, including previously uncharacterized sites and regulatory processes. Notably, extensive citrullination of linker histone H1 and structural proteins such as lamin B1 in ionomycin-activated neutrophils suggests broad remodeling of cell architecture via citrullination. This workflow enables proteome-wide mapping of citrullination sites and facilitates its study across diverse biological contexts.

biochemistry↗

bioRxiv2024

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on [~]53,000 spectra from [~]2,500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of [~]200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

bioinformatics↗