Harnessing the potential of artificial neural networks for predicting protein glycosylation

Pavlos Kotidis; Cleo Kontoravdi

doi:10.1016/j.mec.2020.e00131

Harnessing the potential of artificial neural networks for predicting protein glycosylation

Metab Eng Commun. 2020 May 15:10:e00131. doi: 10.1016/j.mec.2020.e00131. eCollection 2020 Jun.

Authors

Pavlos Kotidis¹, Cleo Kontoravdi¹

Affiliation

¹ Department of Chemical Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom.

Abstract

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.

Keywords: Antibody; Artificial neural networks; Chinese hamster ovary cells; Fusion protein; Hybrid modelling; Nucleotide sugars; Protein glycosylation.