Automated Motif Discovery from Glycan Array Data

Abstract

Assessing interactions of a glycan-binding protein (GBP) or lectin with glycans on a microarray generates large datasets, making it difficult to identify a glycan structural motif or determinant associated with the highest apparent binding strength of the GBP. We have developed a computational method, termed GlycanMotifMiner, that uses the relative binding of a GBP with glycans within a glycan microarray to automatically reveal the glycan structural motifs recognized by a GBP. We implemented the software with a web-based graphical interface for users to explore and visualize the discovered motifs. The utility of GlycanMotifMiner was determined using five plant lectins, SNA, HPA, PNA, Con A, and UEA-I. Data from the analyses of the lectins at different protein concentrations were processed to rank the glycans based on their relative binding strengths. The motifs, defined as glycan substructures that exist in a large number of the bound glycans and few non-bound glycans, were then discovered by our algorithm and displayed in a web-based graphical user interface ( http://glycanmotifminer.emory.edu ). The information is used in defining the glycan-binding specificity of GBPs. The results were compared to the known glycan specificities of these lectins generated by manual methods. A more complex analysis was also carried out using glycan microarray data obtained for a recombinant form of human galectin-8. Results for all of these lectins show that GlycanMotifMiner identified the major motifs known in the literature along with some unexpected novel binding motifs.