ArticleMethods in molecular biology (Clifton, N.J.)2024
Liquid Glycan Array.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The M13 phage platform is a stable and monodisperse nanoscale carrier, which can be modified with different molecules by chemical conjugation strategies. Here, we describe M13 phage acylated on pVIII protein with a dibenzocyclooctyne reacting with azido glycan to yield 30-1500 copy numbers of glycan per phage and monitored by MALDI-TOF spectrometry to generate multivalent glycoconjugates that contain desired densities of glycans. We prepared the liquid glycan arrays (LiGA) such that both the structure and density of glycans were encoded in the DNA of the bacteriophage. The LiGA can be used to validate the binding properties of glycans to purified lectins and explore the effect of glycan density on such binding. From a mixture of multivalent glycan probes, LiGAs can also identify the glycoconjugates with optimal avidity necessary for binding to lectins on living cells in vitro and live animals in vivo.
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