Comprehensive analysis of lectin-glycan interactions reveals determinants of lectin specificity

Abstract

Lectin-glycan interactions facilitate inter- and intracellular communication in many processes including protein trafficking, host-pathogen recognition, and tumorigenesis promotion. Specific recognition of glycans by lectins is also the basis for a wide range of applications in areas including glycobiology research, cancer screening, and antiviral therapeutics. To provide a better understanding of the determinants of lectin-glycan interaction specificity and support such applications, this study comprehensively investigates specificity-conferring features of all available lectin-glycan complex structures. Systematic characterization, comparison, and predictive modeling of a set of 221 complementary physicochemical and geometric features representing these interactions highlighted specificity-conferring features with potential mechanistic insight. Univariable comparative analyses with weighted Wilcoxon-Mann-Whitney tests revealed strong statistical associations between binding site features and specificity that are conserved across unrelated lectin binding sites. Multivariable modeling with random forests demonstrated the utility of these features for predicting the identity of bound glycans based on generalized patterns learned from non-homologous lectins. These analyses revealed global determinants of lectin specificity, such as sialic acid glycan recognition in deep, concave binding sites enriched for positively charged residues, in contrast to high mannose glycan recognition in fairly shallow but well-defined pockets enriched for non-polar residues. Focused fine specificity analysis of hemagglutinin interactions with human-like and avian-like glycans uncovered features representing both known and novel mutations related to shifts in influenza tropism from avian to human tissues. As the approach presented here relies on co-crystallized lectin-glycan pairs for studying specificity, it is limited in its inferences by the quantity, quality, and diversity of the structural data available. Regardless, the systematic characterization of lectin binding sites presented here provides a novel approach to studying lectin specificity and is a step towards confidently predicting new lectin-glycan interactions.

Glycoinformatics in the Artificial Intelligence Era

“…Therefore, several machine and deep learning approaches have recently joined the statistical motif-counting methods to analyze glycan-binding data. An example is a recent study 140 that used the structural data of UniLectin3D to train random forest-based models on computed physicochemical and geometric features of proteins to predict their binding to observed glycan fragments.…”

Programmable Proteins: Target Specificity, Programmability and Future Directions

“…Moreover, other common protein scaffolds, including the protein-guided programmable proteins described in Section 3, can be employed to acquire a novel glycan-binding specificity. Lastly, a plethora of new computational and data science approaches can be used to expand the toolboxes to distinguish further complex glycans [90,92,99], along with the state-of-the-art progress in glycoconjugates research [84].…”

The crystal structure of Nictaba reveals its carbohydrate-binding properties and a new lectin dimerization mode

“…Indeed, lectin domains from different families can recognize and bind similar carbohydrate structures. Though original studies focused on the interaction of lectins with monosaccharides, functional studies highlighted that the natural ligands for lectins are more likely complex glycan structures (Mattox & Bailey-Kellogg, 2021; Van Damme, 2014).…”