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Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant

Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial frag...

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Autores principales: Yu, Fengchao, Haynes, Sarah E., Teo, Guo Ci, Avtonomov, Dmitry M., Polasky, Daniel A., Nesvizhskii, Alexey I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Biochemistry and Molecular Biology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996969/
https://www.ncbi.nlm.nih.gov/pubmed/32616513
http://dx.doi.org/10.1074/mcp.TIR120.002048
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author Yu, Fengchao
Haynes, Sarah E.
Teo, Guo Ci
Avtonomov, Dmitry M.
Polasky, Daniel A.
Nesvizhskii, Alexey I.
author_facet Yu, Fengchao
Haynes, Sarah E.
Teo, Guo Ci
Avtonomov, Dmitry M.
Polasky, Daniel A.
Nesvizhskii, Alexey I.
author_sort Yu, Fengchao
collection PubMed
description Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.
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spelling pubmed-79969692021-04-12 Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant Yu, Fengchao Haynes, Sarah E. Teo, Guo Ci Avtonomov, Dmitry M. Polasky, Daniel A. Nesvizhskii, Alexey I. Mol Cell Proteomics Technological Innovation and Resources Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis. American Society for Biochemistry and Molecular Biology 2020-11-25 /pmc/articles/PMC7996969/ /pubmed/32616513 http://dx.doi.org/10.1074/mcp.TIR120.002048 Text en © 2020 © 2020 Yu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Technological Innovation and Resources
Yu, Fengchao
Haynes, Sarah E.
Teo, Guo Ci
Avtonomov, Dmitry M.
Polasky, Daniel A.
Nesvizhskii, Alexey I.
Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title_full Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title_fullStr Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title_full_unstemmed Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title_short Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
title_sort fast quantitative analysis of timstof pasef data with msfragger and ionquant
topic Technological Innovation and Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996969/
https://www.ncbi.nlm.nih.gov/pubmed/32616513
http://dx.doi.org/10.1074/mcp.TIR120.002048
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