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

• Published in: Journal of proteome research Vol. 20; no. 10; pp. 4708 - 4717
• Main Authors: Kudriavtseva, Polina, Kashkinov, Matvey, Kertész-Farkas, Attila
• Format: Journal Article
• Language: English
• Published: American Chemical Society 01.10.2021
• Subjects: deep learning; PSM scoring; fast; tandem mass spectrometry; convolutional neural networks; spectrum annotation
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.1c00315

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.