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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".