Back

Programming N-linked glycan composition through de novo protein design

Lunn-Halbert, M. C.; Allen, J. D.; Krishna, R.; Manchenko, A.; Li, X.; Lopatto, E. D.; Pinkner, J.; Moller, C.; Ahern, W.; Mendoza, J.; Kang, A.; Nguyen, H.; Joyce, E.; Bera, A.; Hultgren, S. J.; Sokurenko, E.; Crispin, M.; Baker, D.; King, N. P.

2026-10-08 biochemistry
10.64898/2026.10.07.757460 bioRxiv
Show abstract

N-linked glycans are ubiquitous post-translational modifications that mediate diverse biological processes, including cell signaling and adhesion, molecular recognition, and host-pathogen interactions. Despite their importance, N-linked glycans have largely remained beyond the reach of biomolecular design due to a lack of methods capable of jointly modeling glycans and protein structure. Here, we develop a general all-atom computational pipeline for creating de novo glycoproteins in which the amino acid sequence encodes both the location and composition of N-linked glycans. The method uses three-dimensional protein structure to tune glycan accessibility to processing enzymes in the eukaryotic secretory pathway. Systematic increases in steric restriction progressively reduced processing, shifting glycans from heterogeneous complex-type structures toward increasingly underprocessed oligomannose-type species resembling those found on many viral glycoproteins. Despite this programmed restriction, the resulting oligomannose glycans remained accessible for biological recognition, inhibiting uropathogenic Escherichia coli adhesion to human bladder cells and binding mannose-binding lectin. These results establish steric control of N-glycan processing as a genetically encodable design principle and extend computational protein design to functional glycoproteins with programmed composition.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.