Boltzmann Model Predicts Glycan Structures from Lectin Binding

Glycans are complex oligosaccharides
that are involved in many
diseases and biological processes. Unfortunately, current methods
for determining glycan composition and structure (glycan sequencing)
are laborious and require a high level of expertise. Here, we assess
the feasibility of sequencing glycans based on their lectin binding
fingerprints. By training a Boltzmann model on lectin binding data,
we predict the approximate structures of 88 ± 7% of N-glycans
and 87 ± 13% of O-glycans in our test set. We show that our model
generalizes well to the pharmaceutically relevant case of Chinese
hamster ovary (CHO) cell glycans. We also analyze the motif specificity
of a wide array of lectins and identify the most and least predictive
lectins and glycan features. These results could help streamline glycoprotein
research and be of use to anyone using lectins for glycobiology.