LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.
bioinformatics
carbohydrate
computational biology
glycobiology
machine learning
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
revised:
03
11
2021
received:
30
08
2021
pubmed:
5
12
2021
medline:
15
3
2022
entrez:
4
12
2021
Statut:
ppublish
Résumé
Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.
Identifiants
pubmed: 34862760
doi: 10.1002/advs.202103807
pmc: PMC8728848
doi:
Substances chimiques
Lectins
0
Polysaccharides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2103807Informations de copyright
© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.
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