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GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We des...

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Detalles Bibliográficos
Autores principales: Liang, Chenguang, Chiang, Austin W.T., Lewis, Nathan E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160804/
https://www.ncbi.nlm.nih.gov/pubmed/37086409
http://dx.doi.org/10.1016/j.xpro.2023.102244
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author Liang, Chenguang
Chiang, Austin W.T.
Lewis, Nathan E.
author_facet Liang, Chenguang
Chiang, Austin W.T.
Lewis, Nathan E.
author_sort Liang, Chenguang
collection PubMed
description Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).(1)
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spelling pubmed-101608042023-05-06 GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data Liang, Chenguang Chiang, Austin W.T. Lewis, Nathan E. STAR Protoc Protocol Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).(1) Elsevier 2023-04-21 /pmc/articles/PMC10160804/ /pubmed/37086409 http://dx.doi.org/10.1016/j.xpro.2023.102244 Text en © 2023 The Author(s) https://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 Protocol
Liang, Chenguang
Chiang, Austin W.T.
Lewis, Nathan E.
GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title_full GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title_fullStr GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title_full_unstemmed GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title_short GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data
title_sort glycomme, a markov modeling platform for studying n-glycosylation biosynthesis from glycomics data
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160804/
https://www.ncbi.nlm.nih.gov/pubmed/37086409
http://dx.doi.org/10.1016/j.xpro.2023.102244
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