TY  - JOUR
AU  - Declercq, Arthur
AU  - Devreese, Robbe
AU  - Scheid, Jonas
AU  - Jachmann, Caroline
AU  - Van Den Bossche, Tim
AU  - Preikschat, Annica
AU  - Gómez-Zepeda, David
AU  - Rijal, Jeewan Babu
AU  - Hirschler, Aurélie
AU  - Krieger, Jonathan R
AU  - Srikumar, Tharan
AU  - Rosenberger, George
AU  - Martelli, Claudia
AU  - Trede, Dennis
AU  - Carapito, Christine
AU  - Tenzer, Stefan
AU  - Walz, Juliane S
AU  - Degroeve, Sven
AU  - Bouwmeester, Robbin
AU  - Martens, Lennart
AU  - Gabriels, Ralf
TI  - TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore.
JO  - Journal of proteome research
VL  - 24
IS  - 3
SN  - 1535-3893
CY  - Washington, DC
PB  - ACS Publications
M1  - DKFZ-2025-00320
SP  - 1067-1076
PY  - 2025
N1  - HI-TRON / 2025 Mar 7;24(3):1067-1076
AB  - The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.
KW  - DDA-PASEF (Other)
KW  - machine learning (Other)
KW  - mass spectrometry (Other)
KW  - peptide identification (Other)
KW  - proteomics (Other)
KW  - rescoring (Other)
KW  - timsTOF (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:39915959
DO  - DOI:10.1021/acs.jproteome.4c00609
UR  - https://inrepo02.dkfz.de/record/298620
ER  -