Investigation of the Immunogenicity and Specificity of the Transferrin Binding Proteins from Bovine Pathogenic Bacteria
Bibliographic record
Abstract
Through trying to survive and proliferate within their hosts, pathogens have developed creative mechanisms of acquiring iron. A nutrient required for survival by most organisms, iron is both highly sought and jealously guarded. Hosts sequester iron using iron-binding proteins. These iron sinks keep the extracellular concentration of free iron at a level that does not support microbial proliferation. Transferrin is a bilobal iron carrying protein found in the blood and cerebrospinal fluid, and on mucosal surfaces. It is targeted by a number of microbial transferrin binding proteins and siderophores. This thesis focuses on one of the best-studied bacterial transferrin receptors, which consists of the transporter TbpA and its associated surface lipoprotein TbpB. TbpA and TbpB have been studied for years as potential vaccine antigens. Recently, mutants of TbpB that do not bind transferrin have been shown to provide improved protection against infection compared to wild-type TbpB. In this thesis, I develop a method for rapidly screening TbpB mutants, measure loss of affinity for multiple mutations, and investigate the effectiveness of a mutant in generating transferrin blocking antibodies. Furthermore, I investigate multiple methods of measuring binding specificity in Tbp-transferrin interactions in order to lay the groundwork for identifying functionally important residues in Tbps, and for designing improved animal models of infection.
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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".