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Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics

Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learni...

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Autores principales: Sun, Weiping, Zhang, Qianqiu, Zhang, Xiyue, Tran, Ngoc Hieu, Ziaur Rahman, M., Chen, Zheng, Peng, Chao, Ma, Jun, Li, Ming, Xin, Lei, Shan, Baozhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329677/
https://www.ncbi.nlm.nih.gov/pubmed/37422459
http://dx.doi.org/10.1038/s41467-023-39699-5
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author Sun, Weiping
Zhang, Qianqiu
Zhang, Xiyue
Tran, Ngoc Hieu
Ziaur Rahman, M.
Chen, Zheng
Peng, Chao
Ma, Jun
Li, Ming
Xin, Lei
Shan, Baozhen
author_facet Sun, Weiping
Zhang, Qianqiu
Zhang, Xiyue
Tran, Ngoc Hieu
Ziaur Rahman, M.
Chen, Zheng
Peng, Chao
Ma, Jun
Li, Ming
Xin, Lei
Shan, Baozhen
author_sort Sun, Weiping
collection PubMed
description Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.
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spelling pubmed-103296772023-07-10 Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics Sun, Weiping Zhang, Qianqiu Zhang, Xiyue Tran, Ngoc Hieu Ziaur Rahman, M. Chen, Zheng Peng, Chao Ma, Jun Li, Ming Xin, Lei Shan, Baozhen Nat Commun Article Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies. Nature Publishing Group UK 2023-07-08 /pmc/articles/PMC10329677/ /pubmed/37422459 http://dx.doi.org/10.1038/s41467-023-39699-5 Text en © Crown 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sun, Weiping
Zhang, Qianqiu
Zhang, Xiyue
Tran, Ngoc Hieu
Ziaur Rahman, M.
Chen, Zheng
Peng, Chao
Ma, Jun
Li, Ming
Xin, Lei
Shan, Baozhen
Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title_full Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title_fullStr Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title_full_unstemmed Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title_short Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
title_sort glycopeptide database search and de novo sequencing with peaks glycanfinder enable highly sensitive glycoproteomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329677/
https://www.ncbi.nlm.nih.gov/pubmed/37422459
http://dx.doi.org/10.1038/s41467-023-39699-5
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