Structural Basis for Evasion of Nutritional Immunity by the Pathogenic Neisseriae
Bibliographic record
Abstract
The pathogenic Neisseria species are human-adapted pathogens that cause quite distinct diseases. N. gonorrhoeae causes the common sexually-transmitted infection, gonorrhea while N. meningitidis causes a potentially-lethal form of bacterial meningitis. During infection, both pathogens deploy a number of virulence factors in order to thrive in the host. The focus of this review is on the outer membrane transport systems that enable the Neisseriae to utilize host-specific nutrients, including metal-binding proteins such as transferrin and calprotectin. Because acquisition of these critical metals is essential for growth and survival, understanding the structures of receptor-ligand complexes may be an important step in developing preventative or therapeutic strategies focused on thwarting these pathogens. Much can also be learned by comparing structures with antigenic diversity among the transporter sequences, as conserved functional domains in these essential transporters could represent the pathogens’ “Achilles heel”. Towards this goal, we present known or modeled structures for the transport systems produced by the pathogenic Neisseria species, overlapped with sequence diversity derived by comparing hundreds of neisserial protein sequences. Given the concerning increase in N. gonorrhoeae incidence and antibiotic resistance, these outer membrane transport systems appear to be excellent targets for new therapies and preventative vaccines.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".