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Global profiling of arginine reactivity and ligandability in the human proteome

Nature Chemistry (2026)Cite this article

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Abstract

Despite the crucial biological functions of arginine, its reactivity and ligandability within the human proteome remain largely unexplored. Here we apply activity-based protein profiling (ABPP) with phenylglyoxal-based chemical probes to map arginine reactivity globally. Screening phenylglyoxal derivatives identified a probe with enhanced coverage and selectivity, enabling quantification of 4,606 arginine sites across human cell lines. Among these, critical residues regulate liquid–liquid phase separation. Arginine reactivity was further assessed by on-beads reductive dimethylation proteomics, revealing a subset of hyper-reactive sites. Competitive fragment screening using data-independent acquisition ABPP (DIA-ABPP) generated a ligandability map of arginine residues across 60 dicarbonyl compounds. This dataset revealed ligandable arginines that modulate protein activity, in particular protein–protein interactions, highlighting potential covalent drug targets. Together, this work provides a proteome-wide profile of arginine reactivity and ligandability, offering insights into the functional landscape of arginines and expanding the scope of covalent drug discovery to include arginine-targeting molecules.

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Fig. 1: Screening of PG derivatives for arginine profiling in the human proteomes.
Fig. 2: Bioinformatics analysis of labelled arginine functions.
Fig. 3: Quantitative profiling of arginine reactivity in the human proteome.
Fig. 4: Arginine–π interaction mediated phase separation.
Fig. 5: A global map of glyoxal compounds–arginine reactivity in the human proteome.
Fig. 6: Perturbation of PPIs by arginine-targeting ligands.

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Data availability

The MS data generated in this study have been deposited to the ProteomeXchange Consortium via the iProX74 partner repository with the dataset identifier PXD056202 (Arginine_Profiling_MS dataset). Source data are provided with this paper.

Code availability

The Python code and the corresponding dataset have been deposited to Zenodo at https://doi.org/10.5281/zenodo.15493178 (ref. 75). Alternatively, it is available from the corresponding author upon request.

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Acknowledgements

We are grateful for financial support of this work from Shenzhen Bay Laboratory Startup (21240041 to G.L.), Guangdong Special Support Plan for Outstanding Young Talents (2023TQ07A238 to G.L.) and the Natural Science Foundation of Guangdong Province (2023A1515111118 to Y.W.). We thank the Multi-omics Mass Spectrometry Core Facility at the Bio-Tech Center of Shenzhen Medical Academy of Research and Translation (SMART) for their technical support. We thank Z. Li (Jinan University) for providing the S-(4-nitrophenacyl)glutathione (4-NPG) substrate. We thank Y. Jiang, L. Fu, W. Xiao, Y. Chen, T. Guo and X. Guo for helpful discussions. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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  1. These authors contributed equally: Yuena Wang, Tao Hu.

Authors and Affiliations

  1. Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, China

    Yuena Wang, Tao Hu, Lin Zhu, Shaoshuai Xie, Xi Yang, Caifeng Xu, Yansheng Zhai, Youming Li, Xiaoyan Huang & Gang Li

  2. College of Bee Science and Biomedicine, Fujian Agriculture and Forest University, Fuzhou, China

    Yuena Wang

  3. State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Life Sciences, Northwest A & F University, Yangling, China

    Tao Hu & Bo Yang

  4. Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China

    Lin Zhu

  5. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Youming Li

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Contributions

G.L. conceived the study and supervised the research. Y.W., T.H., Y.Z., X.H., B.Y. and G.L. designed and analysed the biological experiments. L.Z., C.X. and G.L. designed and synthesized the chemical compounds. X.Y., Y.L. and S.X. developed the Python and R code for data processing, bioinformatics and chemoinformatics analyses. Y.W. and G.L. wrote the paper with input from all authors.

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Correspondence to Gang Li.

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Extended data

Extended Data Fig. 1 Specificity analysis of the probe 1 and 2.

a) Distribution of peptide modifications for probe 1 and 2, considering localized peptide spectrum matches (PSMs) exceeding 80. b) Proposed structures of the probe 1-derived adducts corresponding to the mass shifts of +318.1117 Da and its oxidized form (+334.1066 Da). c) Proposed structures of the probe 2-derived adducts corresponding to the mass shifts of +276.0997 Da and its oxidized product (+292.0961 Da). d) Probe selectivity analysis using an open-search method. e) Selectivity of probe 1 and 2 determined through mass-offset search using the mass shifts in (a). f) Number of modification sites identified by a closed-search using the expected differential masses for each probe.

Source data

Extended Data Fig. 2 Strategies to enhance arginine labeling sites.

a) Identification of arginine labeling sites in membrane and soluble fractions using probe 1, with two biological replicates. b) Comparison of arginine labeling sites using an alternative workflow where trypsin digestion precedes enrichment, versus the traditional approach where enrichment precedes digestion. Two biological replicates were performed. c) Overlap of the arginine sites labelled in the human proteome across two biological replicates. d) Proposed chemical structure of the PhGO-alkyne derived adducts. e) Comparison of arginine-modified sites identified by probe 1 and PhGO-alkyne in HeLa cell lysates. f) Overlap of arginine sites labelled by probe 1 and PhGO-alkyne across the human proteome.

Source data

Extended Data Fig. 3 Live cell arginine labeling by probe 1.

a) Optimization of probe 1 concentration and incubation time for live-cell labeling. b) Number of arginine-modified sites identified in live cells via closed-search using the expected mass shift of probe 1. c) Cytotoxicity of probe 1 in HeLa cells measured by CellTiter-Glo assay. d) Intracellular concentration of probe 1 in HeLa cells determined by targeted LC–MS/MS. Data are shown as mean ± SD from three biologically independent replicates.

Source data

Extended Data Fig. 4 Quantitative profiling of arginine reactivity in the human proteome.

a) Waterfall plot of log2(R10:1) values from two biological replicates, representing the probe-modified arginine sites used to quantify arginine reactivity across the proteome. b-d) Validation of labeling for proteins containing hyper-reactive arginine residues (GSHR R81, RIR1 R284, and FSCN1 R217). Recombinant wild-type (WT) proteins and corresponding arginine-to-lysine (R-to-K) mutants were expressed in HEK293T proteomes, labelled with probe 1, and analyzed via in-gel fluorescence. e) Western blots were used to confirm protein expressions for LLPS assays. All data are representative of two biologically independent experiments (n = 2).

Source data

Extended Data Fig. 5 Arginine-π interaction mediated phase separation.

a, c, e) Fluorescence imaging comparing wild-type and mutant cells, confirming the functional role of R269 in UBQL2 (a), R313 in NONO (c) and H90 in SFPQ (e) for promoting phase separation. Scale bar, 5 μm. n = 3 biological independent replicates. b, d, f) Quantification of puncta number and size in wild-type and mutant cells (UBQL2 R269A in b, NONO R313A in d, and SFPQ H90A in f) after arsenite treatment. Boxes represent the interquartile range (25th-75th percentiles), and center lines denote the median. Statistical significance was assessed using unpaired two-tailed Student’s t-tests; P values are indicated.

Source data

Extended Data Fig. 6 Chemical structures of glyoxal compounds used in this study.

Glyoxal compounds are categorized into three structural types: aryl-disubstituted glyoxals (Type 1), alkyl-disubstituted glyoxals (Type 2), and monosubstituted glyoxals (Type 3).

Extended Data Fig. 7 Validation of liganded arginines in cell lysate.

a) Representative MS1 extracted ion chromatograms (XIC) showing decreased probe 1-labelled peptide signal upon competition with CP 1 for KAD1. Average RDMSO/CP values, calculated from biological duplicates, are displayed below the XIC. b) Western blot validation of liganded arginines in HEK293T cell lysates expressing wild-type or arginine-to-alanine mutant KAD1, showing selective blockade of probe 1 labeling by CP 1. c) Structural model of KAD1 (PDB: 2C95) highlighting the liganded arginine residue. d) Bar plots illustrating the effects of CP 1, R81A and R81W mutation on the redox-dependent enzymatic activities of GSHR. Data are shown as mean ± SD from at least three biologically independent replicates.

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Wang, Y., Hu, T., Zhu, L. et al. Global profiling of arginine reactivity and ligandability in the human proteome. Nat. Chem. (2026). https://doi.org/10.1038/s41557-025-02012-6

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