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

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708–4717.

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

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: deep learning, Deep Convolutional Neural Networks, PSM scores, spectrum annotation, tandem mass spectrometry

Publication based on the results of: Еnd-to-end learning for spectrometry data annotation (2021)