Genetically Encoded Fragment-Based Discovery of Glycopeptide Ligands for Carbohydrate-Binding Proteins
Bibliographic record
Abstract
We describe an approach to accelerate the search for competitive inhibitors for carbohydrate-recognition domains (CRDs). Genetically encoded fragment-based discovery (GE-FBD) uses selection of phage-displayed glycopeptides to dock a glycan fragment at the CRD and guide selection of synergistic peptide motifs adjacent to the CRD. Starting from concanavalin A (ConA), a mannose (Man)-binding protein, as a bait, we narrowed a library of 10(8) glycopeptides to 86 leads that share a consensus motif, Man-WYD. Validation of synthetic leads yielded Man-WYDLF that exhibited 40-50-fold enhancement in affinity over methyl α-d-mannopyranoside (MeMan). Lectin array suggested specificity: Man-WYD derivative bound only to 3 out of 17 proteins—ConA, LcH, and PSA—that bind to Man. An X-ray structure of ConA:Man-WYD proved that the trimannoside core and Man-WYD exhibit identical CRD docking, but their extra-CRD binding modes are significantly different. Still, they have comparable affinity and selectivity for various Man-binding proteins. The intriguing observation provides new insight into functional mimicry of carbohydrates by peptide ligands. GE-FBD may provide an alternative to rapidly search for competitive inhibitors for lectins.
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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) | 0.000 | 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".