A Machine Learning Based Approach to de novo Sequencing of Glycans from Tandem Mass Spectrometry Spectrum

Abstract

Recently, glycomics has been actively studied and various technologies for glycomics have been rapidly developed. Currently, tandem mass spectrometry (MS/MS) is one of the key experimental tools for identification of structures of oligosaccharides. MS/MS can observe MS/MS peaks of fragmented glycan ions including cross-ring ions resulting from internal cleavages, which provide valuable information to infer glycan structures. Thus, the aim of de novo sequencing of glycans is to find the most probable assignments of observed MS/MS peaks to glycan substructures without databases. However, there are few satisfiable algorithms for glycan de novo sequencing from MS/MS spectra. We present a machine learning based approach to de novo sequencing of glycans from MS/MS spectrum. First, we build a suitable model for the fragmentation of glycans including cross-ring ions, and implement a solver that employs Lagrangian relaxation with a dynamic programming technique. Then, to optimize scores for the algorithm, we introduce a machine learning technique called structured support vector machines that enable us to learn parameters including scores for cross-ring ions from training data, i.e., known glycan mass spectra. Furthermore, we implement additional constraints for core structures of well-known glycan types including N-linked glycans and O-linked glycans. This enables us to predict more accurate glycan structures if the glycan type of given spectra is known. Computational experiments show that our algorithm performs accurate de novo sequencing of glycans. The implementation of our algorithm and the datasets are available at http://glyfon.dna.bio.keio.ac.jp/.