Abstract

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.