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A Hydrogen-Deuterium Exchange Mass Spectrometry HDX-MS Platform for Investigating Peptide Biosynthetic Enzymes

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

Jianbai Ye¹, Xiangnan He¹, Shujuan Wang²

¹MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China.

Journal of Proteome Research

|December 28, 2023

Summary

This summary is machine-generated.

Deep learning in proteomics improves peptide identification using test-time training (PepT3). This method enhances accuracy by adapting models to specific experimental data, boosting discovery in immunopeptidomics.

Keywords:

out-of-distribution peptide identification peptide-spectrum matching spectrum prediction test-time training

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Published on: November 15, 2017

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Peptide-spectrum matching is crucial for identifying peptides and proteins in bottom-up proteomics.
Deep learning models predict tandem mass spectra for peptide-spectrum matching but struggle with data variability.
Differences in sample types, enzyme specificity, and instrument calibration cause inaccurate predictions in standard supervised learning models.

Purpose of the Study:

To develop a novel approach for improving peptide identification in proteomics.
To address the limitations of general deep learning models in handling experimental data variations.
To introduce PepT3, a test-time training paradigm for adaptive peptide-spectrum matching.

Main Methods:

Developed PepT3, a test-time training paradigm to adapt pretrained deep learning models.
Implemented adaptive modeling to generate experimental data-specific models for peptide-spectrum matching.
Applied PepT3 to standard and patient-derived immunopeptidomic samples.

Main Results:

PepT3 increased peptide identification by 10-40% depending on data variability.
In immunopeptidomics, PepT3 identified 60% more tumor-specific immunopeptide candidates.
Two-thirds of newly identified candidates were predicted to bind to patient's human leukocyte antigen isoforms.

Conclusions:

Test-time training (PepT3) significantly enhances peptide and protein identification in proteomics.
PepT3 improves the discovery of tumor-specific immunopeptides, crucial for cancer research.
The PepT3 model and results are publicly archived on Zenodo.org (identifier 8231084).