A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities

• DOI: 10.1021/acschembio.1c00689
• Publication Date: 2022-01-27T17:53:05Z
• Authors: Daniel Bojar; Lawrence Meche; Guanmin Meng; William Eng; David F. Smith; Richard D. Cummings; Lara K. Mahal
• Keywords: Glycomics; Glycobiology; Glycome

Glycans are critical to every facet of biology and medicine, from viral infections embryogenesis. Tools study glycans rapidly evolving; however, the majority our knowledge is deeply dependent on binding by glycan proteins (e.g., lectins). The specificities lectins, which often naturally isolated proteins, have not been well-defined, making it difficult leverage their full potential for analysis. Herein, we use a combination machine learning algorithms expert annotation define lectin specificity this important probe set. Our analysis uses comprehensive microarray commercially available lectins obtained using version 5.0 Consortium Functional Glycomics (CFGv5). This data set was made public in 2011. We report creation its large-scale evaluation lectin-glycan behaviors. motif performed integrating 68 manually defined features with systematic probing computational rules significant motifs mono- disaccharides linkages. Combining manual annotation, create detailed interpretation glycan-binding 57 unique categorized major motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid sulfate, GlcNAc chitin, Gal LacNAc, GalNAc. work provides fresh insights into complex current use, providing guide these reagents.