Combined Analysis of Multiple Glycan-Array Datasets: New Explorations of Protein–Glycan Interactions

Glycan
arrays are indispensable for learning about the specificities
of glycan-binding proteins. Despite the abundance of available data,
the current analysis methods do not have the ability to interpret
and use the variety of data types and to integrate information across
datasets. Here, we evaluated whether a novel, automated algorithm
for glycan-array analysis could meet that need. We developed a regression-tree
algorithm with simultaneous motif optimization and packaged it in
software called MotifFinder. We applied the software to analyze data
from eight different glycan-array platforms with widely divergent
characteristics and observed an accurate analysis of each dataset.
We then evaluated the feasibility and value of the combined analyses
of multiple datasets. In an integrated analysis of datasets covering
multiple lectin concentrations, the software determined approximate
binding constants for distinct motifs and identified major differences
between the motifs that were not apparent from single-concentration
analyses. Furthermore, an integrated analysis of data sources with
complementary sets of glycans produced broader views of lectin specificity
than produced by the analysis of just one data source. MotifFinder,
therefore, enables the optimal use of the expanding resource of the
glycan-array data and promises to advance the studies of protein–glycan
interactions.