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

ABSTRACT: 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.

INSTRUMENT(S): timsTOF HT

ORGANISM(S): Homo Sapiens (ncbitaxon:9606) Three-species Mixture Hye

SUBMITTER: Bernhard Kuster  

PROVIDER: MSV000096926 | MassIVE | Fri Jan 24 01:41:00 GMT 2025

SECONDARY ACCESSION(S): PXD060140

REPOSITORIES: MassIVE