Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Abstract

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.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

“…While the authors demonstrated that Mistle works well in this use case, extrapolating such a library to all possible HLA class I peptides from a canonical human proteome would require generating, storing, and handling an msp file with more than 250 GB in size, storing upwards of 150 M spectra for ∼45 M unmodified peptide sequences. This size would increase even further when considering possible PTMs ( 116 ), mutations ( 115 ), or even cryptic HLA peptides ( 32 ). Furthermore, such libraries will likely have to be generated for every mass spectrometer and various instrument settings separately in order to maintain the high specificity of the library.…”