Journal
ANALYTICAL CHEMISTRY
Volume 89, Issue 23, Pages 12690-12697Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.7b02566
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Funding
- National Key Research and Development Program of China [2016YFA0501301]
- National Key Research Program of China [2016YFB1000605]
- National Natural Science Foundation of China [31470805]
- CAS Interdisciplinary Innovation Team Program
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In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electrontransfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q or even I = L), which were very difficult to distinguish using traditional search engines.
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