LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.

ABSTRACT: Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions a

SUBMITTER: Lundstrom J 

PROVIDER: S-EPMC8728848 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature