Linear and neural network models for predicting N-glycosylation in Chinese Hamster Ovary cells based on B4GALT levels

Glycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, data-driven models to predict quantitative N-glycan distributions have been lacki...

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Bibliographic Details
Published in:Computers & chemical engineering 2025-03, Vol.194, p.108937, Article 108937
Main Authors: Seber, Pedro, Braatz, Richard D.
Format: Article
Language:English
Subjects:
Biopharmaceuticals
Chinese hamster ovary
Deep learning
Glycosylation
Machine learning
N-glycosylation
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Summary:Glycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, data-driven models to predict quantitative N-glycan distributions have been lacking. This article constructs linear and neural network models to predict the distribution of glycans on N-glycosylation sites. The models are trained on data containing normalized B4GALT1–B4GALT4 levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 1.59% on an independent test set, an error 9-fold smaller than for previously published models using the same data, and a narrow error distribution. We also discuss issues with other models in the literature and the advantages of this work’s model over other data-driven models. We openly provide all of the software used, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2024.108937