MassIVE MSV000096926

Partial Public PXD060140

SWAPS: a modular deep-learning empowered peptide identity propagation framework beyond match-between-run

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Description

Mass spectrometry (MS)-based proteomics relies heavily on MS/MS (MS2) data, which does not fully exploit the available MS1 information. Traditional peptide identification 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 post-processing to enable and explore PIP across more diverse experimental conditions and LC gradients. SWAPS substantially enhances precursor identifications, especially in shorter gradients. On the example of 30-, 15-, and 7.5-minute gradients, SWAPS achieves increases of 46.3%, 86.2%, and 112.1% on precursor-level over MS2-based identifications from MaxQuant. 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 yet 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. [doi:10.25345/C5M03Z89R] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: SWAPS ; timsTOF ; MS1-based ; Match-Between-Runs ; DatasetType:Proteomics

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Principal Investigators:
(in alphabetical order) 		Bernhard Kuster, Chair of Proteomics and Bioanalytics, Technical University of Munich, Germany
Submitting User: KusterLab
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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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