Mutational Tuning of Galectin-3 Specificity and Biological Function
Bibliographic record
Abstract
Galectins are defined by a conserved β-galactoside binding site that has been linked to many of their important functions in e.g. cell adhesion, signaling, and intracellular trafficking. Weak adjacent sites may enhance or decrease affinity for natural β-galactoside-containing glycoconjugates, but little is known about the biological role of this modulation of affinity (fine specificity). We have now produced 10 mutants of human galectin-3, with changes in these adjacent sites that have altered carbohydrate-binding fine specificity but that retain the basic β-galactoside binding activity as shown by glycan-array binding and a solution-based fluorescence anisotropy assay. Each mutant was also tested in two biological assays to provide a correlation between fine specificity and function. Galectin-3 R186S, which has selectively lost affinity for LacNAc, a disaccharide moiety commonly found on glycoprotein glycans, has lost the ability to activate neutrophil leukocytes and intracellular targeting into vesicles. K176L has increased affinity for β-galactosides substituted with GlcNAcβ1-3, as found in poly-N-acetyllactosaminoglycans, and increased potency to activate neutrophil leukocytes even though it has lost other aspects of galectin-3 fine specificity. G182A has altered carbohydrate-binding fine specificity and altered intracellular targeting into vesicles, a possible link to the intracellular galectin-3-mediated anti-apoptotic effect known to be lost by this mutant. Finally, the mutants have helped to define the differences in fine specificity shown by Xenopus, mouse, and human galectin-3 and, as such, the evidence for adaptive change during evolution.
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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) | 0.001 | 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".