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Stochastic Context-Free Graph Grammars for Glycoprotein Modelling

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Implementation and Application of Automata (CIAA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3317))

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Abstract

The rapid progress in proteomics has generated an increased interest in the full characterization of glycoproteins. Tandem mass spectrometry is a useful technique. One common problem of current bioinformatics tools for automated interpretation of tandem mass spectra of glycoproteins is that they often give many candidates of oligosaccharide structures with very close scores. We propose an alternative approach in which stochastic context-free graph grammars are used to model oligosaccharide structures. Our stochastic model receives as input structures of known glycans in the library to train the probability parameters of the grammar. After training, the method uses the learned rules to predict the structure of glycan given a composition of unknown glycoprotein. Preliminary results show that integrating such modelling with the automated interpretation software program, GlycoMaster, can very accurately elucidate oligosaccharide structures with tandem mass spectra. This paper describes the stochastic graph grammars modelling glycoproteins.

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Author information

Authors and Affiliations

  1. Dept. Computer Science, Univ. of Western Ontario, London, ON, N6A 5B7, Canada

    Baozhen Shan

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  1. Baozhen Shan
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Editors and Affiliations

  1. Department of Computer Science, University of Manitoba, R3T 2N2, Winnipeg, MB, Canada

    Michael Domaratzki

  2. Department of Mathematics, University of Turku, Finland

    Alexander Okhotin

  3. School of Computing, Queen’s University, K7L 3N6, Kingston, Ontario, Canada

    Kai Salomaa

  4. Department of Computer Science, University of Western Ontario, N6A 5B7, London, Ontario, Canada

    Sheng Yu

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© 2005 Springer-Verlag Berlin Heidelberg

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Shan, B. (2005). Stochastic Context-Free Graph Grammars for Glycoprotein Modelling. In: Domaratzki, M., Okhotin, A., Salomaa, K., Yu, S. (eds) Implementation and Application of Automata. CIAA 2004. Lecture Notes in Computer Science, vol 3317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30500-2_23

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  • DOI: https://doi.org/10.1007/978-3-540-30500-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24318-2

  • Online ISBN: 978-3-540-30500-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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