The CHO VRC01 cell line produces an anti-HIV IgG1 monoclonal antibody containing N-linked glycans on both the Fab (variable) and Fc (constant) regions. Site-specific glycan analysis was used to measure the complex effects of cell culture process conditions on Fab and Fc glycosylation. Experimental data revealed major differences in glycan fractions across the two sites. Bioreactor pH was found to influence fucosylation, galactosylation, and sialylation in the Fab region and galactosylation in the Fc region. To understand the complex effects of process conditions on site-specific N-linked glycosylation, a kinetic model of site-specific N-linked glycosylation was developed. The model parameters provided mechanistic insights into the differences in glycan fractions observed in the Fc and Fab regions. Enzyme activities calculated from the model provided insights into the effect of bioreactor pH on site-specific N-linked glycosylation. Model predictions were experimentally tested by measuring glycosyltransferase-enzyme mRNA-levels and intracellular nucleotide sugar concentrations. The model was used to demonstrate the effect of increasing galactosyltransferase activity on site-specific N-linked glycan fractions. Experiments involving galactose and MnCl 2 supplementation were used to test model predictions. The model is capable of providing insights into experimentally measured data and also of making predictions that can be used to design media supplementation strategies.

N-linked glycosylation is a process in which glycans are added to the nitrogen atom of the asparagine amino acid of the protein. It has a significant impact on protein function, stability, and immunogenicity. Modelling N-glycosylation is a complex task due to the diversity of glycan structures, the variability of glycosylation sites, and the heterogeneity of the glycosylation process. Various computational models have been developed to predict N-glycosylation patterns and understand the relationship between glycan structures and biological function. These models use different methods, such as molecular dynamics simulations, protein engineering, and machine learning, to study the glycosylation process and predict the glycan structures. Understanding the factors influencing N-glycosylation can provide insights into developing therapeutic interventions for diseases like inflammation, viral infections, and cancer. This paper reviews the current state of modelling N-glycosylation and highlights recent advances in this field.