A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry

Yin Wu; Yehia Mechref; Iveta Klouckova; Milos V. Novotny; Haixu Tang

doi:10.1007/978-3-540-73060-6_7

A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry

Yin Wu, Yehia Mechref, Iveta Klouckova, Milos V. Novotny, Haixu Tang

Chemistry and Biochemistry

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Glycosylation is one of the most common post-translational modifications (PTMs) of proteins, the characterization of which is commonly achieved utilizing mass spectrometry (MS). However, its applicability is currently limited by the lack of computational tools capable of autmoated interpretation of high throughput MS experiments which would allow the characterization of glycosylation sites and their microheterogeneities. We present here a computational approach which overcomes this problem and allows the identification and assignment of the microheterogeneities of glycosylation sites of glycoproteins from liquid chromatography ion-trap-based mass spectrometry (LC/MS) data. This method was implemented in a software tool and tested on several model glycoproteins. The results demonstrate the potential of our computational approach in automating the high throughput identification of glycoproteins.

Original language	English
Title of host publication	Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers
Publisher	Springer-Verlag
Pages	96-107
Number of pages	12
ISBN (Print)	9783540730590
DOIs	https://doi.org/10.1007/978-3-540-73060-6_7
State	Published - 2007
Event	Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics - San Diego, CA, United States Duration: Dec 1 2006 → Dec 3 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4532 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics
Country	United States
City	San Diego, CA
Period	12/1/06 → 12/3/06

Keywords

Algorithm
Glycoproteomics
Mass spectrometry
Site-specific glycosylation

Access to Document

10.1007/978-3-540-73060-6_7

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Wu, Y., Mechref, Y., Klouckova, I., Novotny, M. V., & Tang, H. (2007). A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry. In Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers (pp. 96-107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4532 LNBI). Springer-Verlag. https://doi.org/10.1007/978-3-540-73060-6_7

Wu, Yin ; Mechref, Yehia ; Klouckova, Iveta ; Novotny, Milos V. ; Tang, Haixu. / A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry. Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers. Springer-Verlag, 2007. pp. 96-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Glycosylation is one of the most common post-translational modifications (PTMs) of proteins, the characterization of which is commonly achieved utilizing mass spectrometry (MS). However, its applicability is currently limited by the lack of computational tools capable of autmoated interpretation of high throughput MS experiments which would allow the characterization of glycosylation sites and their microheterogeneities. We present here a computational approach which overcomes this problem and allows the identification and assignment of the microheterogeneities of glycosylation sites of glycoproteins from liquid chromatography ion-trap-based mass spectrometry (LC/MS) data. This method was implemented in a software tool and tested on several model glycoproteins. The results demonstrate the potential of our computational approach in automating the high throughput identification of glycoproteins.",

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Wu, Y, Mechref, Y, Klouckova, I, Novotny, MV & Tang, H 2007, A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry. in Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4532 LNBI, Springer-Verlag, pp. 96-107, Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, San Diego, CA, United States, 12/1/06. https://doi.org/10.1007/978-3-540-73060-6_7

A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry. / Wu, Yin; Mechref, Yehia; Klouckova, Iveta; Novotny, Milos V.; Tang, Haixu.

Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers. Springer-Verlag, 2007. p. 96-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4532 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Novotny, Milos V.

AU - Tang, Haixu

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N2 - Glycosylation is one of the most common post-translational modifications (PTMs) of proteins, the characterization of which is commonly achieved utilizing mass spectrometry (MS). However, its applicability is currently limited by the lack of computational tools capable of autmoated interpretation of high throughput MS experiments which would allow the characterization of glycosylation sites and their microheterogeneities. We present here a computational approach which overcomes this problem and allows the identification and assignment of the microheterogeneities of glycosylation sites of glycoproteins from liquid chromatography ion-trap-based mass spectrometry (LC/MS) data. This method was implemented in a software tool and tested on several model glycoproteins. The results demonstrate the potential of our computational approach in automating the high throughput identification of glycoproteins.

AB - Glycosylation is one of the most common post-translational modifications (PTMs) of proteins, the characterization of which is commonly achieved utilizing mass spectrometry (MS). However, its applicability is currently limited by the lack of computational tools capable of autmoated interpretation of high throughput MS experiments which would allow the characterization of glycosylation sites and their microheterogeneities. We present here a computational approach which overcomes this problem and allows the identification and assignment of the microheterogeneities of glycosylation sites of glycoproteins from liquid chromatography ion-trap-based mass spectrometry (LC/MS) data. This method was implemented in a software tool and tested on several model glycoproteins. The results demonstrate the potential of our computational approach in automating the high throughput identification of glycoproteins.

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Wu Y, Mechref Y, Klouckova I, Novotny MV, Tang H. A computational approach for the identification of site-specific protein glycosylations through ion-trap mass spectrometry. In Systems Biology and Computational Proteomics - Joint RECOMB 2006 Satellite Workshops on Systems Biology and on Computational Proteomics, Revised Selected Papers. Springer-Verlag. 2007. p. 96-107. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-73060-6_7