Spectral libraries are useful resources
in proteomic data analysis.
Recent advances in deep learning allow tandem mass spectra of peptides
to be predicted from their amino acid sequences. This enables predicted
spectral libraries to be compiled, and searching against such libraries
has been shown to improve the sensitivity in peptide identification
over conventional sequence database searching. However, current prediction
models lack support for longer peptides, and thus far, predicted library
searching has only been demonstrated for backbone ion-only spectrum
prediction methods. Here, we propose a deep learning-based full-spectrum
prediction method to generate predicted spectral libraries for peptide
identification. We demonstrated the superiority of using full-spectrum
libraries over backbone ion-only prediction approaches in spectral
library searching. Furthermore, merging spectra from different prediction
models, as a form of ensemble learning, can produce improved spectral
libraries, in terms of identification sensitivity. We also show that
a hybrid library combining predicted and experimental spectra can
lead to 20% more confident identifications over experimental library
searching or sequence database searching.