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

J Proteome Res. 2021 Oct 1;20(10):4708-4717. doi: 10.1021/acs.jproteome.1c00315. Epub 2021 Aug 27.

Abstract

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.

Keywords: PSM scoring; convolutional neural networks; deep learning; fast; spectrum annotation; tandem mass spectrometry.

MeSH terms

  • Algorithms
  • Databases, Factual
  • Neural Networks, Computer*
  • Peptides
  • Tandem Mass Spectrometry*

Substances

  • Peptides