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

ABSTRACT: 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 M

SUBMITTER: Gabriel W 

PROVIDER: S-EPMC11925583 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature