1
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Siraj A, Bouwmeester R, Declercq A, Welp L, Chernev A, Wulf A, Urlaub H, Martens L, Degroeve S, Kohlbacher O, Sachsenberg T. Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links. Proteomics 2024; 24:e2300144. [PMID: 38629965 DOI: 10.1002/pmic.202300144] [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: 05/27/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 04/19/2024]
Abstract
In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.
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Affiliation(s)
- Arslan Siraj
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Robbin Bouwmeester
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Arthur Declercq
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Luisa Welp
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Bioanalytics, Institute of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Aleksandar Chernev
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Alexander Wulf
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Bioanalytics, Institute of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Lennart Martens
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Sven Degroeve
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
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2
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Picciani M, Gabriel W, Giurcoiu VG, Shouman O, Hamood F, Lautenbacher L, Jensen CB, Müller J, Kalhor M, Soleymaniniya A, Kuster B, The M, Wilhelm M. Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit. Proteomics 2024; 24:e2300112. [PMID: 37672792 DOI: 10.1002/pmic.202300112] [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: 06/14/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/08/2023]
Abstract
Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.
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Affiliation(s)
- Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Wassim Gabriel
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Victor-George Giurcoiu
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Omar Shouman
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Firas Hamood
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Cecilia Bang Jensen
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Julian Müller
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Armin Soleymaniniya
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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3
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Gabriel W, Picciani M, The M, Wilhelm M. Deep Learning-Assisted Analysis of Immunopeptidomics Data. Methods Mol Biol 2024; 2758:457-483. [PMID: 38549030 DOI: 10.1007/978-1-0716-3646-6_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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4
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McGann CD, Barshop WD, Canterbury JD, Lin C, Gabriel W, Huang J, Bergen D, Zabrouskov V, Melani RD, Wilhelm M, McAlister GC, Schweppe DK. Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics. J Proteome Res 2023; 22:2836-2846. [PMID: 37557900 DOI: 10.1021/acs.jproteome.3c00085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Sample multiplexed quantitative proteomics assays have proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor coisolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Toward this end, we sought to develop and implement a new real-time spectral library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time compared to traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy for sample multiplexed analyses.
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Affiliation(s)
- Chris D McGann
- University of Washington, Seattle, Washington 98105, United States
| | | | | | - Chuwei Lin
- University of Washington, Seattle, Washington 98105, United States
| | | | - Jingjing Huang
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - David Bergen
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Vlad Zabrouskov
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Rafael D Melani
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | | | - Devin K Schweppe
- University of Washington, Seattle, Washington 98105, United States
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5
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Geer LY, Lapin J, Slotta DJ, Mak TD, Stein SE. AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence. J Proteome Res 2023; 22:2246-2255. [PMID: 37232537 PMCID: PMC10542943 DOI: 10.1021/acs.jproteome.2c00807] [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] [Indexed: 05/27/2023]
Abstract
The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.
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Affiliation(s)
- Lewis Y. Geer
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Joel Lapin
- Department of Physics, Georgetown University, Washington, DC 20057, United States
- Associate, Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Douglas J. Slotta
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Tytus D. Mak
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Stephen E. Stein
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
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6
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Searle BC, Shannon AE, Wilburn DB. Scribe: Next Generation Library Searching for DDA Experiments. J Proteome Res 2023; 22:482-490. [PMID: 36695531 DOI: 10.1021/acs.jproteome.2c00672] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.
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Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States.,Proteome Software Inc., Portland, Oregon97219, United States
| | - Ariana E Shannon
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
| | - Damien Beau Wilburn
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
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7
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Cormican JA, Horokhovskyi Y, Soh WT, Mishto M, Liepe J. inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction. Mol Cell Proteomics 2022; 21:100432. [PMID: 36280141 PMCID: PMC9720494 DOI: 10.1016/j.mcpro.2022.100432] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.
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Affiliation(s)
- John A Cormican
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Yehor Horokhovskyi
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Wai Tuck Soh
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom; The Francis Crick Institute, London, United Kingdom.
| | - Juliane Liepe
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany.
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8
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Bittremieux W, Wang M, Dorrestein PC. The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics 2022; 18:94. [PMID: 36409434 DOI: 10.1007/s11306-022-01947-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/19/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. AIM OF REVIEW We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
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Affiliation(s)
- Wout Bittremieux
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
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9
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Gabriel W, Giurcoiu V, Lautenbacher L, Wilhelm M. Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT. Proteomics 2022; 22:e2100257. [PMID: 35578405 DOI: 10.1002/pmic.202100257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/22/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022]
Abstract
Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3 - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Victor Giurcoiu
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
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