Genetically encoded multivalent liquid glycan array displayed on M13 bacteriophage.

The central dogma of biology does not allow for the study of glycans using DNA sequencing. We report a liquid glycan array (LiGA) platform comprising a library of DNA 'barcoded' M13 virions that display 30-1,500 copies of glycans per phage. A LiGA is synthesized by acylation of the phage pVIII protein with a dibenzocyclooctyne, followed by ligation of azido-modified glycans. Pulldown of the LiGA with lectins followed by deep sequencing of the barcodes in the bound phage decodes the optimal structure and density of the recognized glycans. The LiGA is target agnostic and can measure the glycan-binding profile of lectins, such as CD22, on cells in vitro and immune cells in a live mouse. From a mixture of multivalent glycan probes, LiGAs identify the glycoconjugates with optimal avidity necessary for binding to lectins on living cells in vitro and in vivo.

GlyNet: a multi-task neural network for predicting protein-glycan interactions.

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein-glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.