Ligand Specificity of CS-35, a Monoclonal Antibody That Recognizes Mycobacterial Lipoarabinomannan: A Model System for Oligofuranoside−Protein Recognition
Bibliographic record
Abstract
The CS-35 antibody is widely used in the characterization of glycans containing D-arabinofuranose residues, in particular polysaccharides present in the mycobacterial cell wall. A detailed understanding of the combining site of this antibody and the measurement of its binding to different ligands is of interest as this knowledge will have implications in the characterization of arabinofuranose-containing glycoconjugates that are increasingly recognized as important biological molecules. Of even greater significance is that an in-depth study of this carbohydrate-protein interaction will provide insights into the mechanisms by which oligosaccharides containing furanose rings are bound by proteins, an area that has, to date, received little attention. This system has been refractory to X-ray crystallography, and thus we report here a study of the interaction of CS-35 with its ligands using a combination of chemical synthesis, mass spectrometry, titration microcalorimetry, and NMR spectroscopy. Through these investigations we have established that the binding pocket recognizes, as a minimum epitope, a linear tetrasaccharide motif and that the residues at the reducing and non-reducing end of the oligosaccharide are essential for tight binding. The residue at the non-reducing end appears to be bound in an aliphatic pocket, whereas the rest of the tetrasaccharide interacts more strongly with aromatic amino acids.
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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".