Selection of galectin‐3 ligands derived from genetically encoded glycopeptide libraries
Bibliographic record
Abstract
Abstract In this article, we used genetically encoded fragment‐based discovery (GE‐FBD) approach to identify glycopeptides that bind to the carbohydrate recognition domain of the human galectin‐3 (G3C). We generated 6 chemically identical phage libraries Ser‐[X]4‐Gly‐Gly‐Gly, built on variable combinations of redundant Ser and Gly codons. Oxime ligation of hydroxylamine derivatives of galactose (Gal), glucose (Glu), mannose (Man), rhamnose (Rha), and xylose (Xyl) produced a glycopeptide library in which both peptide and glycan can be decoded via DNA sequencing. Screening of this library against G3C identified 1062 combinations of monosaccharides and peptides that exhibited a significant (P < .05) enrichment on G3C and not control selections. Glycopeptides Gal‐WKPE, Gal‐WHVP, and Gal‐LSMA displayed on phage exhibited up to 63‐fold increase in binding potency to G3C when compared to phage displaying random glycopeptide or nonglycosylated SWKPE, SWHVP, and SLSMA. This work mapped the boundary conditions of the GE‐FBD approach with respect to the affinity of individual fragments. We observed that fragments with no detectable affinity (Glu, Xyl, and Rha) diverted the selection toward ligands that bind to G3C equally well with or without the glycan. Weak fragments (Gal, 10 mM) could effectively steer the selection toward G3C ligands in which glycan and peptide bind synergistically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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 source (direct Gemma or distilled Codex), 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".