Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification

In
bottom-up proteomics, peptide-spectrum matching is critical
for peptide and protein identification. Recently, deep learning models
have been used to predict tandem mass spectra of peptides, enabling
the calculation of similarity scores between the predicted and experimental
spectra for peptide-spectrum matching. These models follow the supervised
learning paradigm, which trains a general model using paired peptides
and spectra from standard data sets and directly employs the model
on experimental data. However, this approach can lead to inaccurate
predictions due to differences between the training data and the experimental
data, such as sample types, enzyme specificity, and instrument calibration.
To tackle this problem, we developed a test-time training paradigm
that adapts the pretrained model to generate experimental data-specific
models, namely, PepT3. PepT3 yields a 10–40% increase in peptide
identification depending on the variability in training and experimental
data. Intriguingly, when applied to a patient-derived immunopeptidomic
sample, PepT3 increases the identification of tumor-specific immunopeptide
candidates by 60%. Two-thirds of the newly identified candidates are
predicted to bind to the patient’s human leukocyte antigen
isoforms. To facilitate access of the model and all the results, we
have archived all the intermediate files in Zenodo.org with identifier 8231084.