Dataset Information

Decoding Post-Translational Modification Patterns to Gain Functional Insights into Pathogenic Missense Variants

ABSTRACT: 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.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Matthew Holt

LAB HEAD: Matthew V Holt

PROVIDER: PXD059468 | Pride | 2025-09-29

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	Chenwei_H1_ph_50percent_1.raw	Raw
	Chenwei_H6_ph_50percent_1.raw	Raw
	Chenwei_H7_ph_50percent_1.raw	Raw
	Chenwei_H8_ph_50percent_1.raw	Raw
	Chenwei_M1_ph_50percent_1.raw	Raw

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Publications

DeepMVP: deep learning models trained on high-quality data accurately predict PTM sites and variant-induced alterations.

Wen Bo B Wang Chenwei C Li Kai K Han Ping P Holt Matthew V MV Savage Sara R SR Lei Jonathan T JT Dou Yongchao Y Shi Zhiao Z Li Yi Y Zhang Bing B

Nature methods 20250826 9

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 datase ...[more]

PMID: 40859022

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