Genetically encoded multivalent liquid glycan array displayed on M13 bacteriophage
Bibliographic record
Abstract
Abnormal cell surface glycosylation plays a major role in disease processes such as immune evasion. However, the underlying role of glycans is yet to be fully understood. Binding information obtained from glycan arrays can provide critical starting points for downstream applications such as the development of carbohydrate‐based inhibitors, vaccines, and other therapeutics. However, it is challenging to use powerful techniques like DNA deep sequencing to analyze glycan recognition due to the lack of 1:1 correspondence between DNA and glycan structures. Therefore, we have developed Li quid G lycan A rray ( LiGA ), a technology that allows for genetic encoding of glycans. LiGA provides a 1:1 correspondence between the glycan displayed in multiple copies on a bacteriophage carrier and the phage genetic material. LiGA is generated by acylation of phage pVIII protein with a dibenzocyclooctyne, followed by ligation of azido‐modified glycans. The display of glycans on each phage virion can be controlled from 30‐1500 copies to probe the critical variables in glycan recognition: valency and density. A simple pulldown of the LiGA along with lectins followed by deep sequencing of the DNA in the bound phage decodes the recognized glycans. LiGA is target agnostic and measures binding profile of lectins expressed on intact cells, such as hCD22 (Siglec‐2) and DC‐SIGN (Dendritic Cell‐Specific Intercellular adhesion molecule‐3‐Grabbing Non‐integrin), and in live mice ( Nat. Chem. Bio. 17, 806–816, 2021). From a mixture of 50‐100 multivalent glycan probes, LiGA identifies the glycan‐phage conjugates with optimal valency and density for binding to antibodies and lectins on cells in vitro and in vivo . Sialic acid‐binding immunoglobulin‐type lectins (Siglecs) expressed on the surface of immune cells are exploited by cancer to evade immune response. We applied LiGA to study the binding specificity of Siglec‐7, a cell surface receptor that cancer cells use to evade immune response from natural killer (NK) cells. Additionally, we explored the roles of valency and density in ganglioside interaction with Siglec‐1 using a cell‐based assay. Building on these successes, we plan to use LiGA to identify the valency and affinity required by trans ‐ glycan to overcome the cis ‐ masking on the surface of immune cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".