GAGrank: Software for Glycosaminoglycan Sequence Ranking Using a Bipartite Graph Model

Area of Science:

• Biochemistry
• Computational Biology
• Analytical Chemistry

Background:

• Sulfated glycosaminoglycans (GAGs) are vital polysaccharides with complex structures impacting physiological processes.
• Electron activated dissociation tandem mass spectrometry (EAD-MS) is effective for GAG analysis, but manual interpretation is challenging.
• Computational tools are needed for efficient and accurate GAG sequencing.

Purpose of the Study:

• To introduce GAGrank, a novel network-based computational method for determining GAG structure from tandem mass spectrometry data.
• To improve the accuracy and efficiency of GAG sequencing compared to manual interpretation.
• To demonstrate GAGrank's capability in analyzing complex GAG structures and isomeric mixtures.

Main Methods:

• Development of GAGrank, a network-based algorithm inspired by Google's PageRank, utilizing BiRank for bipartite networks.
• Integration of GAGfinder, a peak picking and elemental composition assignment algorithm, with GAGrank.
• Optimization of GAGrank parameters using simulated annealing on training sequences.
• Validation of GAGrank performance on independent validation sequences and isomeric mixtures.

Main Results:

• GAGrank successfully determines GAG structure by ranking possible sequences based on their linkage to tandem MS fragments.
• Optimized GAGrank parameters were established using simulated annealing.
• The method was validated on multiple GAG sequences and demonstrated effectiveness in sequencing isomeric mixtures.

Conclusions:

• GAGrank provides an accurate and efficient computational approach for GAG structure determination from EAD-MS data.
• This method addresses the limitations of manual spectral interpretation, facilitating GAG research.
• GAGrank has the potential to advance the study of GAGs in various biological contexts.