• Innovative TIMScore™ algorithm, combined with TIMS DIA- NN deep learning, now integrated into ‘Run & Done’ Bruker PaSER^™2022 proteomics system
• Identifies and quantifies more than 8000 protein groups (PG) in 35 minute-gradient single dia-PASEF runs from ultra-deep libraries containing over 13,000 PGs in cell-line assays with 1% FDR (false discovery rate) specificity
• Over 25% more phosphopeptides identified using TIMScore

At the Proteomic Forum | EuPA 2022, Bruker Corporation (Nasdaq: BRKR) announced expanded capabilities for deeper proteomic and epiproteomic coverage, including enhanced phosphopeptide analysis using the innovative TIMScore algorithm, which is now a part of the new PaSER 2022 GPU-based platform. The novel TIMScore algorithm takes advantage of machine learning (ML) to predict CCS values of tryptic and phosphorylated peptides. Experimentally measured CCS values are referenced against the predicted CCS value to call the most probable assignment, thereby increasing peptide confidence and coverage in high sensitivity applications. The TIMScore algorithm is especially adept in identifying phosphopeptides even at the strictest false localization rate (1% FLR) using LuciPHOr¹, where it typically identifies 10%-25% more phosphopeptides.

The human kinome comprises over 500 kinases and is essential for catalyzing protein phosphorylation, which during dysregulation is a known contributor to oncogenesis.² In a previous human cancer cell line study by Mann et. al., at least three-fourths of the detected proteome (7832 out of 10,801 proteins) were found to be phosphorylated³. In a recent study performed in the Tenzer Lab at the University Medical Center of the Johannes Gutenberg University Mainz, some 27,768 phosphopeptides contained 4,672 isobaric phosphopeptide pairs of which 51% were chromatographically coeluting. TIMScore could separate 19% of the coeluting isomer pairs, refining the view of the phosphoproteome of a human osteosarcoma cell line.

Dr. Stefan Tenzer is Professor of Quantitative Proteomics and the Head of the Mass Spectrometry Core Facility at the University Medical Center of the Johannes Gutenberg University Mainz. Dr. Tenzer commented: “In our lab we use four timsTOF systems to understand posttranslational modifications and signaling pathways. TIMScore in PaSER uses machine learning to predict CCS values of phosphopeptides to decrease peptide ambiguity. The integration of the TIMScore model in PaSER allows for the seamless search of our data and provides an impressive increase of over 25% in the number of identified unique phosphopeptides, enabling deeper phosphoproteome coverage.”

TIMScore complements the capabilities of TIMS DIA-NN. Both have been integrated in PaSER 2022 for “Run & Done” dda-PASEF and dia-PASEF workflows. Using TIMScore and dda-PASEF acquisitions of K562 and MOLT-4 cell lines, 40 fractions run at short (35 min) and long (120 min) gradients and filtered to a 1% FDR⁴ resulted in the identification of more than 513,000 precursors and 13,114 protein groups. With access to this experimentally derived and statistically filtered ultra-deep library, TIMS DIA-NN and short, 35-minute gradient dia-PASEF runs routinely identify >8000 protein groups and >100,000 precursors, setting the stage for high-throughput translational proteomics.

Dr. Rohan Thakur, President of the Bruker Life-Science Mass Spectrometry division, added: “It is exciting to see this greater depth in the biologically relevant phosphoproteome using mass spectrometry. With ultra-deep protein libraries of over 13k protein groups (PGs), our customers are now able to quantify over 8000 PGs with dia-PASEF, using TIMS DIA-NN, short-gradients and small sample amounts for human cell-line studies in single runs. Since mass spectrometry is a direct high-specificity readout of actual peptides and their modifications, and not a surrogate epitope-binding measurement with unknown FDRs, it has become the high-specificity method of choice for the study of the phosphoproteome. With the release of TIMScore and TIMS DIA-NN, large-scale, high-throughput, label free quantitative phosphoproteomic studies essential for cell signaling pathway analysis are now feasible. This will further enhance biomarker discovery in kinase signaling processes for understanding pathobiology, including in cancer.”

The PaSER 2022 software includes TIMScore and TIMS DIA-NN for processing dda-PASEF and dia-PASEF workflows on timsTOF Pro 2, timsTOF SCP and timsTOF fleX systems. PaSER utilizes data streaming for dia-PASEF workflows in real-time, supporting ‘run & done’ for high-throughput 4D proteomics and 4D epiproteomics workflows.

References:

1. Fermin D., Walmsley SJ, Gringras AC., Choi, H., Nesvizhskii AI (2013). LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol. Cell Proteomics. Nov 12(11): 3409-19. Doi 10.1074/mcp/. PMID: 23918812.
2. Human Kinome study, Savage SR, Zhang B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteomics. 2020 Jul 11;17:27. doi: 10.1186/s12014-020-09290-x. PMID: 32676006; PMCID: PMC7353784.
3. Sharma K, D'Souza RC, Tyanova S, Schaab C, Wiśniewski JR, Cox J, Mann M. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 2014 Sep 11;8(5):1583-94. doi: 10.1016/j.celrep.2014.07.036. Epub 2014 Aug 21. PMID: 25159151.
4. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007 Mar;4(3):207-14. doi: 10.1038/nmeth1019. PMID: 17327847.

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