Summary

　My name is Ryu Takayanagi, a second-year master's student at the University of Tokyo, currently interning at digzyme. At university, I have been conducting research related to protein phosphorylation and protein tertiary structures.

In this tech blog, I would like to introduce GlycoSearcher, a new tool we have developed as part of our R&D activities for comprehensive search and visualization of polysaccharides containing target monosaccharides.

　In recent years, research and industrial utilization of polysaccharides, such as starch and dietary fiber, have become increasingly active. There is a growing demand for the development of new saccharides, and polysaccharides, in particular, are gaining attention for their high structural diversity. To meet this need, we have developed GlycoSearcher, a tool for comprehensive search of various polysaccharides.

Description formats and databases of polysaccharides

　The glyco-compounds we are focusing on have already been reported in numbers exceeding hundreds of thousands and have been databased. To selectively identify polysaccharides that fit specific purposes and apply them in fields such as synthesis pathway exploration and enzyme development, a description format that facilitates computational processing and a comprehensive database are essential.

　Various methods are known for describing the structure of glyco-compounds (Figure 1). Formats like SNFG and KCF excel in visualization but are not well-suited for advanced computational processing, such as structural information extraction and comparison. On the other hand, the IUPAC format offers a concise structural representation that is readable by both humans and machines, but it struggles with complex and ambiguous expressions, such as repeating units[1]. Therefore, GlycoSearcher employs the WURCS format, which is well-suited for computational processing and can represent repeating units, along with the GlyTouCan database[2], which collects glyco-compound information in the WURCS format.

(Figure 1)

Search Using GlycoSearcher

　With GlycoSearcher, it is possible to extract polysaccharides that match specific criteria from a vast number of candidates. For example, you can search for polysaccharides containing particular monosaccharide units such as glucose or galactose. Additionally, it includes a filtering function that allows you to limit the monosaccharide units that make up the polysaccharides. This enables you to list polysaccharides that can be synthesized using a specific monosaccharide as a starting material and other selected sugars.

　The results of a search for polysaccharides containing α-glucose are shown below (Figure 2). Out of 219,857 glycan structures, 9,862 polysaccharides containing α-glucose were identified. Further narrowing down the search to polysaccharides consisting only of glucose, galactose, and fructose reduced the number of candidates to 924.

(Figure 2)　

Visualization and feature extraction of polysaccharide structures

　The obtained search results can be effectively visualized and utilized for subsequent applications (Figure 3). By reconstructing polysaccharides in WURCS format as graphs, it is possible to rapidly visualize thousands of search results within minutes. Additionally, for structures with ambiguous repeating units, repeating only a specific number of times allows not only the visualization of the actual structure but also facilitates further computational processing, such as structural comparisons that are challenging when ambiguous.

　Since the polysaccharides in the search results are represented as graphs, feature extraction for polysaccharide structures is also possible. For example, computations can determine whether the obtained polysaccharide structures have glucose units at their termini or include specific structures (motifs). Furthermore, by integrating the hit polysaccharides with various databases such as PubChem[3], it is possible to obtain information on their common names and related enzyme information, thus providing insights into reactions involving the polysaccharides.

(Figure 3)

Conclusion

　The GlycoSearcher we developed allows for comprehensive searching of target polysaccharides from the database and facilitates further computational processing. Additionally, by extracting information from the identified target polysaccharide candidates and obtaining enzyme information predicted to be involved in their synthesis, we have established a system that links to subsequent enzyme design workflows.

Acknowledgments

　The development of GlycoSearcher, including acquiring knowledge about glyco-compounds, was greatly supported by Mr. Isozaki from the Business Development Department. I would like to take this opportunity to express my gratitude.

References

[1] Hosoda, M., & Kinoshita, S. (2021). "Introduction to Glycan-related Informatics." JSBi Bioinformatics Review, 2(1), 87-95.
[2] GlyTouCan. Retrieved from https://glytoucan.org/
[3] PubChem. Retrieved from https://pubchem.ncbi.nlm.nih.gov/