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

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