SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run

TL;DR

SWAPS is a new framework that improves peptide identification in proteomics by using MS1 data and deep learning, outperforming traditional methods in varied experimental conditions.

Contribution

SWAPS introduces a modular, MS1-centric framework for peptide identity propagation that works across diverse LC gradients and improves identification rates significantly.

Findings

SWAPS increases precursor identification by 46.3-112.1% over MS2-based methods in different LC gradients.

SWAPS maintains quantitative accuracy while effectively deconvoluting MS1 signals.

Current peptide property prediction models are not yet fully comparable to experimental data, highlighting a need for improvement.

Abstract

Mass spectrometry (MS)-based proteomics relies heavily
on MS/MS
(MS2) data, which do not fully exploit the available MS1 information.
Traditional peptide identity propagation (PIP) methods, such as match-between-runs
(MBR), are limited to similar runs, particularly with the same liquid
chromatography (LC) gradients, thus potentially underutilizing available
proteomics libraries. We introduce SWAPS, a novel and modular MS1-centric
framework incorporating advances in peptide property prediction, extensive
proteomics libraries, and deep-learning-based postprocessing to enable
and explore PIP across more diverse experimental conditions and LC
gradients. SWAPS substantially enhances precursor identification,
especially in shorter gradients. On the example of 30, 15, and 7.5
min gradients, SWAPS achieves increases of 46.3, 86.2, and 112.1%
on precursor level over MaxQuant’s MS2-based identifications.
Despite the inherent challenges in controlling false discovery rates
(FDR) with MS1-based methods, SWAPS demonstrates strong efficacy in
deconvoluting MS1 signals, offering powerful discrimination and deeper
sequence exploration, while maintaining quantitative accuracy. By
building on and applying peptide property predictions in practical
contexts, SWAPS reveals that current models, while advanced, are still
not fully comparable to experimental measurements, sparking the need
for further research. Additionally, its modular design allows seamless
integration of future improvements, positioning SWAPS as a forward-looking
tool in proteomics.

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