Understanding the bacterial polysaccharide antigenicity of <i>Streptococcus agalactiae</i> versus <i>Streptococcus pneumoniae</i>
Bibliographic record
Abstract
Bacterial surface capsular polysaccharides (CPS) that are similar in carbohydrate sequence may differ markedly in immunogenicity and antigenicity. The structural origin of these phenomena is poorly understood. Such a case is presented by the Gram-positive bacteria Streptococcus agalactiae (Group B Streptococcus; GBS) type III (GBSIII) and Streptococcus pneumoniae (Pn) type 14 (Pn14), which share closely related CPS sequences. Nevertheless, antibodies (Abs) against GBSIII rarely cross-react with the CPS from Pn14. To establish the origin for the variation in CPS antigenicity, models for the immune complexes of CPS fragments from GBSIII and Pn14, with the variable fragment (Fv) of a GBS-specific mAb (mAb 1B1), are presented. The complexes are generated through a combination of comparative Ab modeling and automated ligand docking, followed by explicitly solvated 10-ns molecular dynamics simulations. The relationship between carbohydrate sequence and antigenicity is further quantified through the computation of interaction energies using the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method, augmented by conformational entropy estimates. Despite the electrostatic differences between Pn14 and GBSIII CPS, analysis indicates that entropic penalties are primarily responsible for the loss of affinity of the highly flexible Pn14 CPS for mAb 1B1. The similarity of the solution conformation of the relatively rigid GBSIII CPS with that in the immune complex characterizes the previously undescribed 3D structure of the conformational epitope. The analysis provides a comprehensive interpretation for a large body of biochemical and immunological data related to Ab recognition of bacterial polysaccharides and should be applicable to other Ab-carbohydrate 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 | 0.000 |
| 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 | 0.001 |
| 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 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".