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Adams C, Gabriel W, Laukens K, Picciani M, Wilhelm M, Bittremieux W, Boonen K. Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nat Commun 2024; 15:3956. [PMID: 38730277 PMCID: PMC11087512 DOI: 10.1038/s41467-024-48322-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.
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Affiliation(s)
- Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, Antwerp, Belgium.
| | - Kurt Boonen
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium.
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2
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Teschner D, Gomez-Zepeda D, Declercq A, Łącki MK, Avci S, Bob K, Distler U, Michna T, Martens L, Tenzer S, Hildebrandt A. Ionmob: a Python package for prediction of peptide collisional cross-section values. Bioinformatics 2023; 39:btad486. [PMID: 37540201 PMCID: PMC10521631 DOI: 10.1093/bioinformatics/btad486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/30/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023] Open
Abstract
MOTIVATION Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing. RESULTS We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task. AVAILABILITY AND IMPLEMENTATION The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.
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Affiliation(s)
- David Teschner
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - David Gomez-Zepeda
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University, 55128 Mainz, Germany
- Immunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON), 55131 Mainz, Germany
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Gent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Mateusz K Łącki
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University, 55128 Mainz, Germany
| | - Seymen Avci
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Konstantin Bob
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Ute Distler
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University, 55128 Mainz, Germany
| | - Thomas Michna
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University, 55128 Mainz, Germany
- Immunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON), 55131 Mainz, Germany
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Gent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University, 55128 Mainz, Germany
- Immunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON), 55131 Mainz, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
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Luo M, Yin Y, Zhou Z, Zhang H, Chen X, Wang H, Zhu ZJ. A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics. Nat Commun 2023; 14:1813. [PMID: 37002244 PMCID: PMC10066191 DOI: 10.1038/s41467-023-37539-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.
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Affiliation(s)
- Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Xi Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Hongmiao Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, P. R. China.
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4
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High-end ion mobility mass spectrometry: A current review of analytical capacity in omics applications and structural investigations. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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5
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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Bob K, Teschner D, Kemmer T, Gomez-Zepeda D, Tenzer S, Schmidt B, Hildebrandt A. Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data. BMC Bioinformatics 2022; 23:287. [PMID: 35858828 PMCID: PMC9301846 DOI: 10.1186/s12859-022-04833-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 07/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties. Results In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. Real data could be strongly reduced without losing relevant information. Our implementation scaled out up to 32 threads and supports acceleration by GPUs. Conclusions Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Availability Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at https://zenodo.org/record/5036526. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04833-5.
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Affiliation(s)
- Konstantin Bob
- Institute of Computer Science, Johannes Gutenberg University Mainz, D-55128, Mainz, Germany
| | - David Teschner
- Institute of Computer Science, Johannes Gutenberg University Mainz, D-55128, Mainz, Germany
| | - Thomas Kemmer
- Institute of Computer Science, Johannes Gutenberg University Mainz, D-55128, Mainz, Germany
| | - David Gomez-Zepeda
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University Mainz, D-55128, Mainz, Germany.,Immunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON) Mainz, D-55131, Mainz, Germany
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes Gutenberg University Mainz, D-55128, Mainz, Germany.,Immunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON) Mainz, D-55131, Mainz, Germany
| | - Bertil Schmidt
- Institute of Computer Science, Johannes Gutenberg University Mainz, D-55128, Mainz, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University Mainz, D-55128, Mainz, Germany.
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7
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Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2022:e21794. [PMID: 35822576 DOI: 10.1002/mas.21794] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Toufik Mahamdi
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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Willems S, Voytik E, Skowronek P, Strauss MT, Mann M. AlphaTims: Indexing Trapped Ion Mobility Spectrometry-TOF Data for Fast and Easy Accession and Visualization. Mol Cell Proteomics 2021; 20:100149. [PMID: 34543758 PMCID: PMC8526765 DOI: 10.1016/j.mcpro.2021.100149] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 12/27/2022] Open
Abstract
High-resolution MS-based proteomics generates large amounts of data, even in the standard LC-tandem MS configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich datasets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole TOF (Q-TOF) platform as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS), and microseconds (TOF). In our open-source AlphaTims package, data are indexed, accessed, and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse four-dimensional matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in an HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happens in milliseconds. We have found AlphaTims to be a key enabling tool to explore high-dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept 'ecosystem'.
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Affiliation(s)
- Sander Willems
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Eugenia Voytik
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Patricia Skowronek
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximilian T Strauss
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; OmicEra Diagnostics GmbH, Planegg, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Faculty of Health Sciences, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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Meier F, Park MA, Mann M. Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics. Mol Cell Proteomics 2021; 20:100138. [PMID: 34416385 PMCID: PMC8453224 DOI: 10.1016/j.mcpro.2021.100138] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/05/2021] [Accepted: 08/11/2021] [Indexed: 12/14/2022] Open
Abstract
Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation-serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond.
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Affiliation(s)
- Florian Meier
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Functional Proteomics, Jena University Hospital, Jena, Germany.
| | - Melvin A Park
- Bruker Daltonics Inc, Billerica, Massachusetts, USA.
| | - Matthias Mann
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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