Molecular Basis for Recognition of the Cancer Glycobiomarker, LacdiNAc (GalNAc[β1→4]GlcNAc), by Wisteria floribunda Agglutinin
Bibliographic record
Abstract
Aberrant glycosylation and the overexpression of specific carbohydrate epitopes is a hallmark of many cancers, and tumor-associated oligosaccharides are actively investigated as targets for immunotherapy and diagnostics. Wisteria floribunda agglutinin (WFA) is a legume lectin that recognizes terminal N -acetylgalactosaminides with high affinity. WFA preferentially binds the disaccharide LacdiNAc (β-d-GalNAc-[1→4]-d-GlcNAc), which is associated with tumor malignancy in leukemia, prostate, pancreatic, ovarian, and liver cancers and has shown promise in cancer glycobiomarker detection. The mechanism of specificity for WFA recognition of LacdiNAc is not fully understood. To address this problem, we have determined affinities and structure of WFA in complex with GalNAc and LacdiNAc. Affinities toward Gal, GalNAc, and LacdiNAc were measured via surface plasmon resonance, yielding K D values of 4.67 × 10 −4 m, 9.24 × 10 −5 m, and 5.45 × 10 −6 m, respectively. Structures of WFA in complex with LacdiNAc and GalNAc have been determined to 1.80–2.32 Å resolution. These high resolution structures revealed a hydrophobic groove complementary to the GalNAc and, to a minor extent, to the back-face of the GlcNAc sugar ring. Remarkably, the contribution of this small hydrophobic surface significantly increases the observed affinity for LacdiNAc over GalNAc. Tandem MS sequencing confirmed the presence of two isolectin forms in commercially available WFA differing only in the identities of two amino acids. Finally, the WFA carbohydrate binding site is similar to a homologous lectin isolated from Vatairea macrocarpa in complex with GalNAc, which, unlike WFA, binds not only αGalNAc but also terminal Ser/Thr O -linked αGalNAc (Tn antigen).
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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".