Structural and Thermodynamic Studies on Cation−Π Interactions in Lectin−Ligand Complexes: High-Affinity Galectin-3 Inhibitors through Fine-Tuning of an Arginine−Arene Interaction
Bibliographic record
Abstract
The high-resolution X-ray crystal structures of the carbohydrate recognition domain of human galectin-3 were solved in complex with N-acetyllactosamine (LacNAc) and the high-affinity inhibitor, methyl 2-acetamido-2-deoxy-4-O-(3-deoxy-3-[4-methoxy-2,3,5,6-tetrafluorobenzamido]-beta-D-galactopyranose)-beta-D-glucopyranoside, to gain insight into the basis for the affinity-enhancing effect of the 4-methoxy-2,3,5,6-tetrafluorobenzamido moiety. The structures show that the side chain of Arg144 stacks against the aromatic moiety of the inhibitor, an interaction made possible by a reorientation of the side chain relative to that seen in the LacNAc complex. Based on these structures, synthesis of second generation LacNAc derivatives carrying aromatic amides at 3'-C, followed by screening with a novel fluorescence polarization assay, has led to the identification of inhibitors with further enhanced affinity for galectin-3 (K(d) > or = 320 nM). The thermodynamic parameters describing the binding of the galectin-3 C-terminal to selected inhibitors were determined by isothermal titration calorimetry and showed that the affinity enhancements were due to favorable enthalpic contributions. These enhancements could be rationalized by the combined effects of the inhibitor aromatic structure on a cation-Pi interaction and of direct interactions between the aromatic substituents and the protein. The results demonstrate that protein-ligand interactions can be significantly enhanced by the fine-tuning of arginine-arene interactions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".