Selective modifications of lactose and N-acetyllactosamine with sulfate and aromatic bulky groups unveil unique structural insights in galectin-1-ligand recognition
Bibliographic record
Abstract
Galectins, a family of endogenous glycan-binding proteins, play crucial roles in a broad range of physiological and pathological processes. Galectin-1 (Gal-1), a proto-type member of this family, is overexpressed in several cancers and plays critical roles in tumor-immune escape, angiogenesis and metastasis. Thus, generation of high-affinity Gal-1 inhibitors emerges as an attractive therapeutic approach for a wide range of neoplastic conditions. Small-molecule carbohydrate inhibitors based on lactose (Lac) and N-acetyllactosamine (LacNAc) structures have been tested showing different results. In this study, we evaluated Lac- and LacNAc-based compounds with specific chemical modifications at key positions as Gal-1 ligands by competitive solid-phase assays (SPA) and isothermal titration calorimetry (ITC). Both assays showed excellent correlation, highlighting that lactosides bearing bulky aromatic groups at the anomeric carbon and sulfate groups at the O3' position exhibited the highest binding affinities. To dissect the atomistic determinants for preferential affinity of the different tested Gal-1 ligands, molecular docking simulations were conducted and PRODIGY-LIG structure-based method was employed to predict binding affinity in protein-ligand complexes. Notably, calculated binding free energies derived from the molecular docking were in accordance with experimental values determined by SPA and ITC, showing excellent correlation between theoretical and experimental approaches. Moreover, this analysis showed that 3'-O-sulfate groups interact with residues of the Gal-1 subsite B, mainly with Asn33, while the ester groups of the aromatic anomeric group interact with Gly69 and Thr70 at Gal-1 subsite E, extending deeper into the pocket, which could account for the enhanced binding affinity. This study contributes to the rational design of highly optimized Gal-1 inhibitors to be further studied in cancer models and other pathologic conditions.
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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".