Predictions from Deep Learning Propose Substantial Protein-Carbohydrate Interplay.

ABSTRACT: It is a grand challenge to identify all the protein - carbohydrate interactions in an organism. Direct experiments would require extensive libraries of glycans to definitively distinguish binding from non-binding proteins. Computational screening of proteins for carbohydrate-binding provides an attractive and ultimately testable alternative. Recent computational techniques have focused primarily on which protein residues interact with carbohydrates or which carbohydrate species a protein binds to. Current estimates label 1.5 to 5% of proteins as carbohydrate-binding proteins; however, 50-70% of proteins are known to be glycosylated, suggesting a potential wealth of proteins that bind to carbohydrates. We therefore developed a novel dataset and neural network architecture, named Prot

SUBMITTER: Canner SW 

PROVIDER: S-EPMC11952328 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature