Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions
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1-s2.0-S193131282030562X-main.pdf
Description
Published version
Size
8.08 MB
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Unknown
Checksum (MD5)
aae7939f00034d08ee34e1664ef72e0b
Author(s)
Bojar, Daniel
Powers, Rani K
Camacho, Diogo M.
Collins, James J.
Date Issued
2021
Journal
Cell Host and Microbe
Publisher
Elsevier BV
Version
Final published version
Abstract
Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from host-microbe interactions. Here, we present machine learning and bioinformatics methods to leverage the evolutionary information present in glycans to gain insights into how pathogens and commensals interact with hosts. By using techniques from natural language processing, we develop deep-learning models for glycans that are trained on a curated dataset of 19,299 unique glycans and can be used to study and predict glycan functions. We show that these models can be utilized to predict glycan immunogenicity and the pathogenicity of bacterial strains, as well as investigate glycan-mediated immune evasion via molecular mimicry. We also develop glycan-alignment methods and use these to analyze virulence-determining glycan motifs in the capsular polysaccharides of bacterial pathogens. These resources enable one to identify and study glycan motifs involved in immunogenicity, pathogenicity, molecular mimicry, and immune evasion, expanding our understanding of host-microbe interactions.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CHOM.2020.10.004
