Language
English
Publication Date
9-1-2025
Journal
Nature Methods
DOI
10.1038/s41592-025-02797-x
PMID
40859022
PMCID
PMC12446062
PubMedCentral® Posted Date
8-26-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its application to predicting PTM-altering missense variants shows strong concordance with experimental results, validated using literature-curated variants and cancer proteogenomic datasets. Together, PTMAtlas and DeepMVP provide a robust platform for PTM research and a scalable framework for assessing the functional consequences of coding variants through the lens of PTMs.
Keywords
Deep Learning, Protein Processing, Post-Translational, Humans, Phosphorylation, Computational Biology, Software, Databases, Protein, Proteome informatics, Machine learning, Post-translational modifications, Genomics, Proteomics
Published Open-Access
yes
Recommended Citation
Wen, Bo; Wang, Chenwei; Li, Kai; et al., "DeepMVP: Deep Learning Models Trained on High-Quality Data Accurately Predict PTM Sites and Variant-Induced Alterations" (2025). Huffington Center on Aging Staff Publications. 56.
https://digitalcommons.library.tmc.edu/aging_research/56