Imperial College London
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Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
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Location

 

310ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kotidis:2020:10.1016/j.mec.2020.e00131,
author = {Kotidis, P and Kontoravdi, K},
doi = {10.1016/j.mec.2020.e00131},
journal = {Metabolic Engineering Communications},
title = {Harnessing the potential of artificial neural networks for predicting protein glycosylation},
url = {http://dx.doi.org/10.1016/j.mec.2020.e00131},
volume = {10},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
AU  - Kotidis,P
AU  - Kontoravdi,K
DO  - 10.1016/j.mec.2020.e00131
PY  - 2020///
SN  - 2214-0301
TI  - Harnessing the potential of artificial neural networks for predicting protein glycosylation
T2  - Metabolic Engineering Communications
UR  - http://dx.doi.org/10.1016/j.mec.2020.e00131
UR  - http://hdl.handle.net/10044/1/80115
VL  - 10
ER  -
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