Dataset Information

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

ABSTRACT: Citrullination is a key yet underexplored post-translational modification involved in various biological processes. Its identification via mass spectrometry faces challenges like limited enrichment tools and false positives due to mass overlap with deamidation (+0.9840 Da). To address this, we developed a data analysis pipeline integrating the deep learning model Prosit-Cit, trained on ~53,000 spectra from ~2,500 synthetic citrullinated peptides, which improves sensitivity and precision in identifying citrullination sites. This approach has identified up to 14 times more citrullinated sites in human tissue proteomes and revealed new insights, including the first large-scale citrullination mapping in Arabidopsis. This upload includes: 1) Raw files and search SEARCHs from the evaluation dataset, used to assess the precision of citrullination identifications. 2) Raw files, search SEARCHs, and rescoring outcomes from validation experiments conducted on Arabidopsis flowers. 4) Re-analyzed search and rescoring SEARCHs from human (PXD010154) and Arabidopsis (PXD013868) tissue proteomes.

INSTRUMENT(S): Orbitrap Fusion Lumos, Q Exactive HF-X

ORGANISM(S): Homo Sapiens (human) Arabidopsis Thaliana (mouse-ear Cress)

TISSUE(S): Flower, Cell Culture

SUBMITTER: Wassim Gabriel

LAB HEAD: Dr. Chien-Yun Lee

PROVIDER: PXD056560 | Pride | 2025-02-11

REPOSITORIES: pride

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			Action	DRS
	Arabidopsis_30_tissues_MSFragger_100FDR.zip	Other
	Arabidopsis_30_tissues_mzML.zip	Other
	Arabidopsis_Flower_MSFragger_100FDR.zip	Other
	Arabidopsis_Flower_MSFragger_1FDR.zip	Other
	Arabidopsis_Flower_mzML.zip	Other

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Project description:Citrullination is a post-translational modification of arginine catalyzed by five peptidylarginine deiminases (PADs) in humans. The loss of a positive charge may cause structural or functional alterations and while the modification has been linked to several diseases including rheumatoid arthritis and cancer, its physiological or pathophysiological roles remain largely unclear. In part this is owing to limitations in available methodology able to robustly enrich, detect and localize the modification. As a result, only few citrullination sites have been identified on human proteins with high confidence. In this study, we mined data from mass spectrometry-based deep proteomic profiling of 30 human tissues to identify citrullination sites on endogenous proteins. Database searching of ~70 million tandem mass spectra yielded ~13,000 candidate spectra which were further triaged by spectrum quality metrics and the detection of the specific neutral loss of isocyanic acid from citrullinated peptides to reduce false positives. Because citrullination is easily confused with deamidation, we synthetised ~2,200 citrullinated and 1,300 deamindated peptides to build a library of reference spectra. This led to the validation of 375 citrullination sites on 209 human proteins. Further analysis showed that >80% of the identified modifications sites were new and for 56% of the proteins, citrullination was detected for the first time. Sequence motif analysis revealed a strong preference for Asp and Gly, residues around the citrullination site. Interestingly, while the modification was detected in 26 human tissues with the highest levels found in brain and lung, citrullination levels did not correlate well with protein expression of the PAD enzymes. Even though the current work represents the largest survey of protein citrullination to date, the modification was mostly detected on high abundance proteins arguing that the development of specific enrichment methods would be required in order to study the full extent of cellular protein citrullination.

Dataset Information

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

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