Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Authors

Xinpei Yi
Bo Wen
Shuyi Ji
Alexander B Saltzman
Eric J Jaehnig
Jonathan T Lei
Qiang Gao
Bing Zhang

Publication Date

2-1-2024

Journal

Molecular & Cellular Proteomics

DOI

10.1016/j.mcpro.2023.100707

PMID

38154692

PMCID

PMC10831110

PubMedCentral® Posted Date

12-26-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Phosphorylation, Phosphopeptides, Deep Learning, Proteomics, Liver Neoplasms, phosphoproteomics, deep learning, rescore, phosphosite localization, liver cancer, retention time, fragment ion intensity, EGFR, phosphopeptide identification

Abstract

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.