Molecular Insights into O-Linked Sialoglycans Recognition by the Siglec-Like SLBR-N (SLBR<sub>UB10712</sub>) of <i>Streptococcus gordonii</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Streptococcus gordonii is a Gram-positive bacterial species that typically colonizes the human oral cavity, but can also cause local or systemic diseases. Serine-rich repeat (SRR) glycoproteins exposed on the S. gordonii bacterial surface bind to sialylated glycans on human salivary, plasma, and platelet glycoproteins, which may contribute to oral colonization as well as endocardial infections. Despite a conserved overall domain organization of SRR adhesins, the Siglec-like binding regions (SLBRs) are highly variable, affecting the recognition of a wide range of sialoglycans. SLBR-N from the SRR glycoprotein of S. gordonii UB10712 possesses the remarkable ability to recognize complex core 2 O -glycans. We here employed a multidisciplinary approach, including flow cytometry, native mass spectrometry, isothermal titration calorimetry, NMR spectroscopy from both protein and ligand perspectives, and computational methods, to investigate the ligand specificity and binding preferences of SLBR-N when interacting with mono- and disialylated core 2 O -glycans. We determined the means by which SLBR-N preferentially binds branched α2,3-disialylated core 2 O -glycans: a selected conformation of the 3′SLn branch is accommodated into the main binding site, driving the sTa branch to further interact with the protein. At the same time, SLBR-N assumes an open conformation of the CD loop of the glycan-binding pocket, allowing one to accommodate the entire complex core 2 O -glycan. These findings establish the basis for the generation of novel tools for the detection of specific complex O -glycan structures and pave the way for the design and development of potential therapeutics against streptococcal infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".