Non-Carbohydrate Inhibitors of Sialic Acid-binding Immunomodulatory-type Lectin-7 (Siglec-7) Discovered from Genetically Encoded Bicyclic Peptide Libraries
Bibliographic record
Abstract
ABSTRACT Glycan-binding proteins (GBP) are among the most difficult to drug targets. This deficiency delays clinical progress for therapeutically important GBPs. We employed bicyclic genetically encoded libraries (BiGELs), produced by chemical modification of phage-displayed libraries of peptides with two-fold symmetric linchpins, to discover inhibitors of therapeutically relevant Siglec-7:GD3 interactions. Next-generation sequencing (NGS) analysis of panning of BiGEL against Siglec-7 yielded 815 candidates from which 23 hits yielded K D = 1−100 µM as determined by surface plasmon resonance (SPR). Competitive enzyme-linked immunosorbent assays (ELISA) identified a subset of leads that disrupted the Siglec-7:GD3 interaction with IC 50 = 3−300 µM. Machine learning models trained on NGS datasets identified additional inhibitors with equivalent potency. Alanine scans of 8c (SWCRPATVNC, IC 50 = 3.8 µM) and 12c (SFCHYPTHVC, IC 50 = 11 µM), identified key residues as crucial for activity. Ring reshaping studies of compound 8c highlighted the critical role of bicyclic topology produced by analogue 46e (SAAAAAWCRPATVNC, IC 50 = 9.5 µM). Multivalent display of the lead bicycles alongside ∼100 glycans in Liquid glycan Array (LiGA), made it possible to compare the binding of bicycles and glycans to Siglec-7 expressed on CHO, Jurkat, and Raji cells. LiGA assays confirmed binding of the bicycles to Siglec-7 but revealed considerable non-specific interactions with receptor-negative cells. Saturation transfer difference nuclear magnetic resonance (STD-NMR) revealed 46e binds to Siglec-7 at a site distinct from the V-Ig domain, suggesting it might inhibit binding of glycans to the glycan-binding site of Siglec-7 via an allosteric site. Together these results demonstrate that BiGEL enables the discovery of bicyclic peptides for undruggable Siglec targets but highlights future challenges in molecular discoveries that aim to identify small, non-carbohydrate inhibitors of GBPs.
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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 | 0.000 |
| 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".