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Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network
journal contribution
posted on 2020-02-25, 17:11 authored by Kaiyuan Liu, Sujun Li, Lei Wang, Yuzhen Ye, Haixu TangThe ability to predict
tandem mass (MS/MS) spectra from peptide sequences can significantly
enhance our understanding of the peptide fragmentation process and
could improve peptide identification in proteomics. However, current
approaches for predicting high-energy collisional dissociation (HCD)
spectra are limited to predict the intensities of expected ion types,
that is, the a/b/c/x/y/z ions and their neutral loss derivatives (referred
to as backbone ions). In practice, backbone ions
only account for <70% of total ion intensities in HCD spectra,
indicating many intense ions are ignored by current predictors. In
this paper, we present a deep learning approach that can predict the complete spectra (both backbone and nonbackbone ions) directly
from peptide sequences. We made no assumptions or expectations on
which kind of ions to predict but instead predicting the intensities
for all possible m/z. Training this
model needs no annotations of fragment ion nor any prior knowledge
of the fragmentation rules. Our analyses show that the predicted 2+
and 3+ HCD spectra are highly similar to the experimental spectra,
with average full-spectrum cosine similarities of 0.820 (±0.088)
and 0.786 (±0.085), respectively, very close to the similarities
between the experimental replicated spectra. In contrast, the best-performed
backbone only models can only achieve an average similarity below
0.75 and 0.70 for 2+ and 3+ spectra, respectively. Furthermore, we
developed a multitask learning (MTL) approach for predicting spectra
of insufficient training samples, which allows our model to make accurate
predictions for electron transfer dissociation (ETD) spectra and HCD
spectra of less abundant charges (1+ and 4+).