Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles

In recent years, several deep learning-based methods
have been
proposed for predicting peptide fragment intensities. This study aims
to provide a comprehensive assessment of six such methods, namely
Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Prosit Transformer,
and the method proposed by Guan et al. To this end, we evaluated the
accuracy of the predicted intensity profiles for close to 1.7 million
precursors (including both tryptic and HLA peptides) corresponding
to more than 18 million experimental spectra procured from 40 independent
submissions to the PRIDE repository that were acquired for different
species using a variety of instruments and different dissociation
types/energies. Specifically, for each method, distributions of similarity
(measured by Pearson’s correlation and normalized angle) between
the predicted and the corresponding experimental b and y fragment intensities were generated. These
distributions were used to ascertain the prediction accuracy and rank
the prediction methods for particular types of experimental conditions.
The effect of variables like precursor charge, length, and collision
energy on the prediction accuracy was also investigated. In addition
to prediction accuracy, the methods were evaluated in terms of prediction
speed. The systematic assessment of these six methods may help in
choosing the right method for MS/MS spectra prediction for particular
needs.