Glycans, complex sugars covalently attached to proteins, play crucial roles in protein stability and function, participate in “self” recognition, and modulate protein-protein interactions. Unlike proteins, glycans typically do not form secondary structures and remain highly mobile, posing challenges for traditional structural biology techniques. This high mobility, coupled with glycan heterogeneity, complicates the elucidation of complete glycoprotein structures and hinders research on the role of glycans in protein function. Although computer simulations can help address these challenges, they often require millions of hours on specialized supercomputers, highlighting the need for approximate methods that can be integrated into protein-solving pipelines.
To overcome these obstacles, we developed a simplified, open-source method for rapidly predicting the span and shape of glycans with minimal computing power. Using this approach, we can accurately predict SARS-CoV-2 spike protein epitopes that are not shielded by glycans, as well as assess the impact of glycans on protein flexibility. We also demonstrate that this method can be integrated with structural biology methods and glycoproteomics pipelines, offering a comprehensive tool for glycoprotein analysis.
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