Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.13.536762v1?rss=1
Authors: Seber, P., Braatz, R. D.
Abstract:
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, predictive models have been lacking. This article constructs linear and neural network models for the prediction of the distribution of glycans on potential N-glycosylation sites. The models are trained on data containing normalized B4GALT levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 9.10% and a low error distribution, surpassing previously published models. We also discuss issues with other models reported in the literature. We provide all of the software used in this work, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.
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