Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Spectrum
annotation is a challenging task due to the presence of
unexpected peptide fragmentation ions as well as the inaccuracy of
the detectors of the spectrometers. We present a deep convolutional
neural network, called Slider, which learns an optimal feature extraction
in its kernels for scoring mass spectrometry (MS)/MS spectra to increase
the number of spectrum annotations with high confidence. Experimental
results using publicly available data sets show that Slider can annotate
slightly more spectra than the state-of-the-art methods (BoltzMatch,
Res-EV, Prosit), albeit 2–10 times faster. More interestingly,
Slider provides only 2–4% fewer spectrum annotations with low-resolution
fragmentation information than other methods with high-resolution
information. This means that Slider can exploit nearly as much information
from the context of low-resolution spectrum peaks as the high-resolution
fragmentation information can provide for other scoring methods. Thus,
Slider can be an optimal choice for practitioners using old spectrometers
with low-resolution detectors.