Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.

ABSTRACT: Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated

SUBMITTER: Kellman BP 

PROVIDER: S-EPMC11118808 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature