Sep 2026· Journal of the American Chemical Society· 53 references
Glycosylation and Glycoproteins ResearchCarbohydrate Chemistry and Synthesis
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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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.