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Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation

Cite this: J. Proteome Res. 2021, 20, 1, 634–644
Publication Date (Web):September 28, 2020
https://doi.org/10.1021/acs.jproteome.0c00580
Copyright © 2020 American Chemical Society

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    Abstract

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    Liquid chromatography tandem mass spectrometry (LC–MS/MS) has been the most widely used technology for phosphoproteomics studies. As an alternative to database searching and probability-based phosphorylation site localization approaches, spectral library searching has been proved to be effective in the identification of phosphopeptides. However, incompletion of experimental spectral libraries limits the identification capability. Herein, we utilize MS/MS spectrum prediction coupled with spectral matching for site localization of phosphopeptides. In silico MS/MS spectra are generated from peptide sequences by deep learning/machine learning models trained with nonphosphopeptides. Then, mass shift according to phosphorylation sites, phosphoric acid neutral loss, and a “budding” strategy are adopted to adjust the in silico mass spectra. In silico MS/MS spectra can also be generated in one step for phosphopeptides using models trained with phosphopeptides. The method is benchmarked on data sets of synthetic phosphopeptides and is used to process real biological samples. It is demonstrated to be a method requiring only computational resources that supplements the probability-based approaches for phosphorylation site localization of singly and multiply phosphorylated peptides.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00580.

    • Statistics of water neutral loss peaks (Text S1); similarities of predicted spectra to the experimental spectra (Text S2); benchmarks of neutral loss peak generation and budding strategies (Text S3); collision energy (CE) calibration for the HeLa data set (Text S4); analysis of doubly phosphorylated peptides from HeLa cells (Text S5); optimization of CE value for Prosit and pDeep2, as well as phosphoRS parameters on the Syn-OF data set (Figure S1); statistics of water neutral loss peaks (Figure S2); dot product between the extracted peaks from the experimental spectra and the predicted spectra (Figure S3); DeltaDP results of matching the extracted peaks from the experimental spectra and the predicted spectra with different candidate phosphorylation sites (Figure S4); Phosphorylation site localization using MS/MS spectra predicted by pDeep2 with/without buddings (Figure S5); Phosphorylation site localization on Syn-OF, Syn-QE and Syn-TOF using different methods with neutral loss and budding (Figure S6); Localization results on the Syn-QE data set using different methods (Figure S7); Localization results on the Syn-TOF data set using different methods (Figure S8); Distributions of distance between the mislocated phosphorylation sites and the correct sites in Syn-OF using different methods (Figure S9); Optimization of CE value for pDeep2 on the HeLa data set (Figure S10); Phosphorylation site localization of doubly phosphorylated peptides from HeLa cells (Figure S11); LC–MS/MS datasets used in this study (Table S1); probability thresholds at 1% false localization rate (FLR) (Table S2) (PDF)

    • Data Set S1: Phosphorylation site localization results on Syn-OF by Ascore and phosphoRS, as well as spectral matching using MS/MS spectra predicted by MS2PIP, Prosit, DeepMS2, and pDeep2 (XLSX)

    • Data Set S2: Phosphorylation site localization results on Syn-OF using spectral matching by DeepMS2 with different neutral loss ratios (XLSX)

    • Data Set S3: Phosphorylation site localization results on Syn-OF using spectral matching by DeepMS2 with different budding factors (XLSX)

    • Data Set S4: Phosphorylation site localization results on Syn-OF using spectral matching by DeepMS2, MS2PIP, and Prosit with neutral loss generation and budding (XLSX)

    • Data Set S5: Phosphorylation site localization results on Syn-QE by Ascore and phosphoRS, as well as spectral matching using MS/MS spectra predicted by MS2PIP, Prosit, DeepMS2, and pDeep2 (XLSX)

    • Data Set S6: Phosphorylation site localization results on Syn-TOF by Ascore and phosphoRS, as well as spectral matching using MS/MS spectra predicted by MS2PIP, Prosit, DeepMS2, and pDeep2 (XLSX)

    • Data Set S7: Phosphorylation site localization results on Syn-OF, Syn-QE, and Syn-TOF by phosphoRS combined with spectral matching (XLSX)

    • Data Set S8: Phosphorylation site localization results for singly phosphorylated peptides from the HeLa data by phosphoRS, as well as phosphoRS combined with spectral matching (XLSX)

    • Data Set S9: Phosphorylation site localization results for doubly phosphorylated peptides from the HeLa data by phosphoRS, as well as phosphoRS combined with spectral matching (XLSX)

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    Cited By

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    1. Yang Zhang, Benjamin Dreyer, Natalia Govorukhina, Alexander M. Heberle, Saša Končarević, Christoph Krisp, Christiane A. Opitz, Pauline Pfänder, Rainer Bischoff, Hartmut Schlüter, Marcel Kwiatkowski, Kathrin Thedieck, Peter L. Horvatovich. Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics. Analytical Chemistry 2022, 94 (31) , 10893-10906. https://doi.org/10.1021/acs.analchem.2c01036
    2. Zhen-Lin Chen, Peng-Zhi Mao, Wen-Feng Zeng, Hao Chi, Si-Min He. pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning. Journal of Proteome Research 2021, 20 (5) , 2570-2582. https://doi.org/10.1021/acs.jproteome.0c01004
    3. Jürgen Cox. Prediction of peptide mass spectral libraries with machine learning. Nature Biotechnology 2023, 41 (1) , 33-43. https://doi.org/10.1038/s41587-022-01424-w
    4. Patrick Bangert, Nandha Kumar Balasubramaniam, Carol E. Parker, Christoph H. Borchers. Pattern Recognition for Mass-Spectrometry-Based Proteomics. 2022https://doi.org/10.5772/intechopen.108422
    5. Joseph Paul, Timothy D. Veenstra. Separation of Serum and Plasma Proteins for In-Depth Proteomic Analysis. Separations 2022, 9 (4) , 89. https://doi.org/10.3390/separations9040089
    6. Jiří Urban. A review on recent trends in the phosphoproteomics workflow. From sample preparation to data analysis. Analytica Chimica Acta 2022, 1199 , 338857. https://doi.org/10.1016/j.aca.2021.338857
    7. Joanna Bons, Jacob Rose, Amy O’Broin, Birgit Schilling. Advanced mass spectrometry-based methods for protein molecular-structural biologists. 2022, 311-326. https://doi.org/10.1016/B978-0-323-90264-9.00020-9
    8. Yi Yang, Ling Lin, Liang Qiao. Deep learning approaches for data-independent acquisition proteomics. Expert Review of Proteomics 2021, 18 (12) , 1031-1043. https://doi.org/10.1080/14789450.2021.2020654
    9. Fynn M. Hansen, Maria C. Tanzer, Franziska Brüning, Isabell Bludau, Che Stafford, Brenda A. Schulman, Maria S. Robles, Ozge Karayel, Matthias Mann. Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology. Nature Communications 2021, 12 (1) https://doi.org/10.1038/s41467-020-20509-1
    10. Ronghui Lou, Weizhen Liu, Rongjie Li, Shanshan Li, Xuming He, Wenqing Shui. DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation. Nature Communications 2021, 12 (1) https://doi.org/10.1038/s41467-021-26979-1
    11. Matthias Mann, Chanchal Kumar, Wen-Feng Zeng, Maximilian T. Strauss. Artificial intelligence for proteomics and biomarker discovery. Cell Systems 2021, 12 (8) , 759-770. https://doi.org/10.1016/j.cels.2021.06.006

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