UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow

We
present UniSpec, an attention-driven deep neural network designed
to predict comprehensive collision-induced fragmentation spectra,
thereby improving peptide identification in shotgun proteomics. Utilizing
a training data set of 1.8 million unique high-quality tandem mass
spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned
with a peptide fragmentation dictionary encompassing 7919 fragment
peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding
to modification-specific neutral losses. Remarkably, UniSpec can predict
73%–77% of fragment intensities based on our NIST reference
library spectra, a significant leap from the 35%–45% coverage
of only b and y ions. Comparative studies with Prosit elucidate that
while both models are strong at predicting their respective fragment
ion series, UniSpec particularly shines in generating more complex
MS2 spectra with diverse ion annotations. The integration of UniSpec’s
predictions into shotgun proteomics data analysis boosts the identification
rate of tryptic peptides by 48% at a 1% false discovery rate (FDR)
and 60% at a more confident 0.1% FDR. Using UniSpec’s predicted
in-silico spectral library, the search results closely matched those
from search engines and experimental spectral libraries used in peptide
identification, highlighting its potential as a stand-alone identification
tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work
were deposited in Zenodo (https://zenodo.org/records/10052268).