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 nonbound 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.

Novel interactions of complex carbohydrates with peanut (PNA), Ricinus communis (RCA-I), Sambucus nigra (SNA-I) and wheat germ (WGA) agglutinins as revealed by the binding specificities of these lectins towards mucin core-2 O-linked and N-linked glycans and related structures

“…PNA did not recognize any glycan in which the disaccharide motif Galβ1-3GalNAc was sialylated or fucosylated [48].…”

“…It is evident from the glycan array data of Choletti et.al. [48] that SNA-I binds to the trisaccharide determinant NeuAc α2-6 Gal β1-4 GlcNAc when it is present on α1-3 linked Man of the diantennary structure but will not bind to the trisaccharide determinant when it is present on α1-6 linked Man. SNA-I does not bind NeuAc α2-6 Gal β1-4 Glc [48].…”

“…Inspection of the ranking of the glycans bound by PNA through microarray technique [48] indicated that the core-2 O-glycans Galβ1-4 GlcNAc β1-6 (Galβ1-3) GalNAc, Fucα1-2 Galβ1-4 GlcNAc β1-6 (Galβ1-3) GalNAc and Galβ1-3 (GlcNAcβ1-6) GalNAc ranked higher than the disaccharide Galβ1-3GalNAc. PNA did not recognize any glycan in which the disaccharide motif Galβ1-3GalNAc was sialylated or fucosylated [48].…”

Global Comparisons of Lectin–Glycan Interactions Using a Database of Analyzed Glycan Array Data

“…These limitations could be addressed in future developments, such as new glycan array data with expanded glycan repertoires or varying densities (25,29) and the continued definition of motifs that account for complex specificities (21). In addition, we will continue to develop the analysis algorithms, for example, by building in additional statistical analyses or other, recently described approaches for analyzing glycan array data (31,32). The incorporation of new motifs, enhanced analysis methods, and new data from more complex glycan arrays should allow more accurate information to be obtained from the searches.…”

Application of Microarrays for Deciphering the Structure and Function of the Human Glycome

“…The simultaneous analyses of hundreds of unknown glycan structures using multiple analyses of replicate printed arrays before and after single or sequential enzyme digests will result in massive amounts of data that can only be handled computationally. Our initial approach to bioinformatics has developed out of our need to process data from the interaction of unknown GBPs with defined glycan arrays with the purpose to automate the discovery of GBP-binding motifs and define the glycan-binding specificity of GBPs (97). The algorithms used to perform these analyses will be used in developing more sophisticated software to automate the analyses of hundreds of glycans on SGMs.…”