Glycans play critical structural and physiological roles in biological systems, a highly conserved feature that majorly contributes to the complexity across biological systems. In humans, glycans elicit biological responses at multiple scales, ranging from protein-folding quality control within the cell, maintaining cell-cell adhesions, to the recognition of foreign bodies during organ transplants. Glycans pose functional significance in the mechanism of viral immune evasion, as the glycan shield provides a mode of hydrophilicity and conformational entropy, making them a notoriously difficult target to recognize by immune cells. Thus, glycans are an attractive target in understanding disease mechanisms and therapeutic development. However, glycans have been historically understudied due to the limited tools available—largely because glycans are difficult to synthesize and purify due to the high flexibility and structural complexity conferred by their flexible linkages and unique glycan sequences. Here, we take a protein design approach to study the molecular determinants of glycan recognition. Using a combination of our computational protein design pipeline and biophysical characterization of binding capacity through biolayer interferometry (BLI), we aim to create “de novo lectins” that recognize glycans with enhanced affinity and specificity to each glycan subtype. We demonstrate that protein design workflows are capable of recapitulating the binding capabilities of their native scaffolds. Through these workflows, we aim to enhance the native functionalities of lectins beyond evolution. These advances will yield tools that purify glycans with high specificity and precision, expanding the toolkit available for glycobiology research.
Arambulo et al. (Sun,) studied this question.