Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. DMSS has undergone rigorous validation through a series of experiments, consistently demonstrating superior performance compared to current methods in forecasting theoretical spectra. The superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein identification. These findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.
K. Biemann, Mass spectrometry of peptides and proteins, Annu. Rev. Biochem., vol. 61, pp. 977–1010, 1992.
R. Aebersold and M. Mann, Mass spectrometry-based proteomics, Nature, vol. 422, pp. 198–207, 2003.
M. Wilhelm, D. P. Zolg, M. Graber, S. Gessulat, T. Schmidt, K. Schnatbaum, C. Schwencke-Westphal, P. Seifert, N. de Andrade Krätzig, J. Zerweck, et al., Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics, Nat. Commun., vol. 12, no. 1, p. 3346, 2021.
Z. Mao, R. Zhang, L. Xin, and M. Li, Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model, Nat. Mach. Intell., vol. 5, no. 11, pp. 1250–1260, 2023.
J. Cox, Prediction of peptide mass spectral libraries with machine learning, Nature Biotechnology, vol. 41, no. 1, pp. 33–43, 2023.
V. Lange, P. Picotti, B. Domon, and R. Aebersold, Selected reaction monitoring for quantitative proteomics: A tutorial, Mol. Syst. Biol., vol. 4, no. 1, p. 222, 2008.
L. C. Gillet, P. Navarro, S. Tate, H. Röst, N. Selevsek, L. Reiter, R. Bonner, and R. Aebersold, Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis, Mol. Cell. Proteom., vol. 11, no. 6, p. O111.016717, 2012.
P. Sinitcyn, J. D. Rudolph, and J. Cox, Computational methods for understanding mass spectrometry–based shotgun proteomics data, Annu. Rev. Biomed. Data Sci., vol. 1, pp. 207–234, 2018.
J. Cox, N. Neuhauser, A. Michalski, R. A. Scheltema, J. V. Olsen, and M. Mann, Andromeda: A peptide search engine integrated into the MaxQuant environment, J. Proteome Res., vol. 10, no. 4, pp. 1794–1805, 2011.
D. N. Perkins, D. J. C. Pappin, D. M. Creasy, and J. S. Cottrell, Probability-based protein identification by searching sequence databases using mass spectrometry data, 3.0.CO;2-2">Electrophoresis, vol. 20, no. 18, pp. 3551–3567, 1999.
J. K. Eng, A. L. McCormack, and J. R. Yates, An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database, J. Am. Soc. Mass Spectrom., vol. 5, no. 11, pp. 976–989, 1994.
M. Scigelova and A. Makarov, Orbitrap mass analyze—overview and applications in proteomics, Proteomics, vol. 6, no. S2, pp. 16–21, 2006.
J. E. Elias, F. D. Gibbons, O. D. King, F. P. Roth, and S. P. Gygi, Intensity-based protein identification by machine learning from a library of tandem mass spectra, Nat. Biotechnol., vol. 22, no. 2, pp. 214–219, 2004.
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436–444, 2015.
S. Li, R. J. Arnold, H. Tang, and P. Radivojac, On the accuracy and limits of peptide fragmentation spectrum prediction, Anal. Chem., vol. 83, no. 3, pp. 790–796, 2011.
Y. Yu, X. Si, C. Hu, and J. Zhang, A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput., vol. 31, no. 7, pp. 1235–1270, 2019.
X Zhou, W. Zeng, H. Chi, C. Luo, C. Liu, J. Zhan, S. He, and Z. Zhang, pDeep: Predicting MS/MS spectra of peptides with deep learning, Anal. Chem., vol. 89, no. 23, pp. 12690–12697, 2017.
S. Gessulat, T. Schmidt, D. P. Zolg, P. Samaras, K. Schnatbaum, J. Zerweck, T. Knaute, J. Rechenberger, B. Delanghe, A. Huhmer, et al., Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning, Nat. Meth., vol. 16, no. 6, pp. 509–518, 2019.
R. Lou, W. Liu, R. Li, S. Li, X. He, and W. Shui, DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation, Nat. Commun., vol. 12, no. 1, p. 6685, 2021.
M. Ekvall, P. Truong, W. Gabriel, M. Wilhelm, and L. Käll, Prosit Transformer: A transformer for prediction of MS2 spectrum intensities, J. Proteome Res., vol. 21, no. 5, pp. 1359–1364, 2022.
U. H. Toprak, L. C. Gillet, A. Maiolica, P. Navarro, A. Leitner, and R. Aebersold, Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics, Mol. Cell. Proteom., vol. 13, no. 8, pp. 2056–2071, 2014.
D. P. Zolg, M. Wilhelm, K. Schnatbaum, J. Zerweck, T. Knaute, B. Delanghe, D. J. Bailey, S. Gessulat, H.-C. Ehrlich, M. Weininger, et al., Building proteome tools based on a complete synthetic human proteome, Nature Methods, vol. 14, no. 3, pp. 259–262, 2017.
J. V. Olsen, B. Macek, O. Lange, A. Makarov, S. Horning, and M. Mann, Higher-energy C-trap dissociation for peptide modification analysis, Nat. Meth., vol. 4, no. 9, pp. 709–712, 2007.
D. B Bekker-Jensen, C. D Kelstrup, T. S Batth, S. C Larsen, C. Haldrup, J. B Bramsen, K. D Sorensen, S. Hoyer, T. F Orntoft, C. L Andersen, et al., An optimized shotgun strategy for the rapid generation of comprehensive human proteomes, Cell Systems, vol. 4, no. 6, pp. 587–599, 2017.
1166
Views
483
Downloads
0
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).