| BMC Bioinformatics | |
| A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks | |
| Research | |
| Wai-Ki Ching1  Wenpin Hou1  Kiyoko F. Aoki-Kinoshita2  Nobuyuki Hashimoto2  Yushan Qiu3  | |
| [1] Department of Mathematics, The University of Hong Kong, 999077, Hong Kong, China;Faculty of Science and Engineering, Soka University, 192–8577, Tokyo, Japan;Hematology Oncology Division, Northwestern University, IL 60208, Evanston, USA; | |
| 关键词: N; Glycosylation reaction networks construction; Glycosylation enzyme activity; Mass spectrum; Glycobiology; | |
| DOI : 10.1186/s12859-016-1094-6 | |
| 来源: Springer | |
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【 摘 要 】
BackgroundAbnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step.ResultsIn this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems.ConclusionsOur results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.
【 授权许可】
CC BY
© The Author(s) 2016
【 预 览 】
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| RO202311104120585ZK.pdf | 1470KB | ||
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| MediaObjects/12974_2023_2934_MOESM1_ESM.docx | 125KB | Other |
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