Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Yi, X.; Bo, W.; Ji, S.; Saltzman, A. B.; Jaehnig, E. J.; Lei, J. T.; Gao, Q.; Zhang, B.

doi:10.1101/2023.01.11.523329

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bioRxiv · 10.1101/2023.01.11.523329

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Yi, X.·Bo, W.·Ji, S.·Saltzman, A. B.·Jaehnig, E. J.·Lei, J. T.·Gao, Q.·Zhang, B.

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Abstract

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. 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%-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.

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Yi, X., Bo, W., Ji, S., Saltzman, A. B., Jaehnig, E. J., Lei, J. T., Gao, Q., Zhang, B.. 2023-01-12. Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification. https://doi.org/10.1101/2023.01.11.523329

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