Harnessing the potential of artificial neural networks for predicting protein glycosylation

• Published in: Metabolic engineering communications Vol. 10; p. e00131
• Main Authors: Kotidis, Pavlos, Kontoravdi, Cleo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020; Elsevier
• Subjects: algorithms; Antibody; Artificial neural networks; Chinese hamster ovary cells; Cricetulus griseus; Full Length; Fusion protein; glycoproteins; glycosylation; Hybrid modelling; manganese; neural networks; Nucleotide sugars; Protein glycosylation; transport proteins; Hybrid modelling; Antibody; Artificial neural networks; Nucleotide sugars; Protein glycosylation; Fusion protein; Chinese hamster ovary cells

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 tran...