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Using Natural Language Processing to Learn the Grammar of Glycans

View ORCID ProfileDaniel Bojar, View ORCID ProfileDiogo M. Camacho, James J. Collins
doi: https://doi.org/10.1101/2020.01.10.902114
Daniel Bojar
1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
2Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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  • ORCID record for Daniel Bojar
Diogo M. Camacho
1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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James J. Collins
1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
2Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
3Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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  • For correspondence: jimjc@mit.edu
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Abstract

While nucleic acids and proteins receive ample attention, progress on understanding the structural and functional roles of carbohydrates has lagged behind. Here, we develop a language model for glycans, SweetTalk, taking into account glycan connectivity and composition. We use this model to investigate motifs in glycan substructures, classify them according to their O-/N-linkage, and predict their immunogenicity with an accuracy of ∼92%, opening up the potential for rational glycoengineering.

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  • https://github.com/midas-wyss/sweettalk

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 11, 2020.
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Using Natural Language Processing to Learn the Grammar of Glycans
Daniel Bojar, Diogo M. Camacho, James J. Collins
bioRxiv 2020.01.10.902114; doi: https://doi.org/10.1101/2020.01.10.902114
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Using Natural Language Processing to Learn the Grammar of Glycans
Daniel Bojar, Diogo M. Camacho, James J. Collins
bioRxiv 2020.01.10.902114; doi: https://doi.org/10.1101/2020.01.10.902114

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