GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework
File(s)
Published version
Author(s)
Flevaris, Konstantinos
Kotidis, Pavlos
Kontoravdi, Cleo
Type
Journal Article
Abstract
Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings.
Date Issued
2025-02-01
Date Acceptance
2024-08-26
Citation
Analytical and Bioanalytical Chemistry, 2025, 417 (5), pp.957-972
ISSN
1618-2642
Publisher
Springer
Start Page
957
End Page
972
Journal / Book Title
Analytical and Bioanalytical Chemistry
Volume
417
Issue
5
Copyright Statement
© The Author(s) 2024. 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/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39322800
PII: 10.1007/s00216-024-05522-3
Subjects
APPROXIMATE BAYESIAN COMPUTATION
Bayesian inference
Biochemical Research Methods
Biochemistry & Molecular Biology
Chemistry
Chemistry, Analytical
Glycosylation
Graph theory
Kinetic modeling
Life Sciences & Biomedicine
MATHEMATICAL-MODEL
MONTE-CARLO
NOMENCLATURE
Parameter estimation
Physical Sciences
Science & Technology
SELECTION
SYSTEMS
Publication Status
Published
Coverage Spatial
Germany
Date Publish Online
2024-09-26