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Deep learning adds an extra dimension to peptide fragmentation

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The interpretation of fragmentation patterns in tandem mass spectrometry is crucial for peptide sequencing, but the relative intensities of these patterns are difficult to predict computationally. Two groups have applied deep neural networks to address this long-standing problem in the proteomics field, extending theoretical spectra with an additional dimension of high-accuracy fragment ion intensities.

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Fig. 1: Deep neural networks can predict the fragmentation pattern of a peptide from its amino acid sequence alone.

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  1. Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Hannes L. Röst

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  1. Hannes L. Röst
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Correspondence to Hannes L. Röst.

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Röst, H.L. Deep learning adds an extra dimension to peptide fragmentation. Nat Methods 16, 469–470 (2019). https://doi.org/10.1038/s41592-019-0428-5

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