PXD059468

PXD059468 is an original dataset announced via ProteomeXchange.

Title	Decoding Post-Translational Modification Patterns to Gain Functional Insights into Pathogenic Missense Variants
Description	Genome sequencing has uncovered numerous pathogenic missense variants; however, their functional consequences remain largely unexplored, limiting our understanding of their precise roles in diseases. These variants may disrupt post-translational modifications (PTMs), which are crucial for cellular signaling and disease pathogenesis. Here, we present DeepVEP, a computational framework that uses deep learning-based PTM site prediction models to assess the impact of missense variants on six key PTMs. Our PTM site prediction models, trained on 397,524 PTM sites curated in PTMAtlas through systematic reanalysis of 241 PTM-enriched mass spectrometry datasets, significantly outperform existing models. DeepVEP’s variant effect predictions align closely with experimental results, as validated against literature-derived PTM-altering variants and two proteogenomic datasets. Its application to both pathogenic germline and somatic cancer variants creates a comprehensive landscape of PTM-altering disease variants. Furthermore, DeepVEP's interpretability facilitates connecting altered PTMs to potential modifying enzymes, opening new avenues for therapeutic interventions.
HostingRepository	PRIDE
AnnounceDate	2025-09-29
AnnouncementXML	Submission_2025-09-28_16:16:54.365.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Matthew Holt
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	phosphorylated residue
Instrument	Orbitrap Fusion Lumos

Revision	Datetime	Status	ChangeLog Entry
0	2025-01-06 09:56:26	ID requested
⏵ 1	2025-09-28 16:16:55	announced

Wen B, Wang C, Li K, Han P, Holt MV, Savage SR, Lei JT, Dou Y, Shi Z, Li Y, Zhang B, DeepMVP: deep learning models trained on high-quality data accurately predict PTM sites and variant-induced alterations. Nat Methods, 22(9):1857-1867(2025) [pubmed]

10.1038/s41592-025-02797-x;

submitter keyword: machine learning,PTM

Matthew V Holt
contact affiliation	Baylor College of Medicine
contact email	mvholt@bcm.edu
lab head
Matthew Holt
contact affiliation	BCM
contact email	mvholt@bcm.edu
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/09/PXD059468

PRIDE project URI

• PRIDE
  1. PXD059468
     1. Label: PRIDE project
     2. Name: Decoding Post-Translational Modification Patterns to Gain Functional Insights into Pathogenic Missense Variants