Whole‐bacterium ribosome display selection for isolation of highly specific anti‐<i>Staphyloccocus aureus</i> Affitins for detection‐ and capture‐based biomedical applications
Bibliographic record
Abstract
Abstract Detection and capture methods using antibodies have been developed to ensure identification of pathogens in biological samples. Though antibodies have many attractive properties, they also have limitations and there are needs to expand the panel of available affinity proteins with different properties. Affitins, that we developed from the Sul7d proteins, are a solid class of affinity proteins, which can be used as substitutes to antibodies or to complement them. We report the generation and characterization of antibacterial Affitins with high specificity for Staphylococcus aureus . For the first time, ribosome display selections were carried out using whole‐living‐cell and naïve combinatorial libraries, which avoid production of protein targets and immunization of animals. We showed that Affitin C5 exclusively recognizes S. aureus among dozens of strains, including clinical ones. C5 binds staphylococcal Protein A (SpA) with a K D of 108 ± 2 nM and has a high thermostability ( T m = 77.0°C). Anti‐ S. aureus C5 binds SpA or bacteria in various detection and capture applications, including ELISA, western blot analysis, bead‐fishing, and fluorescence imaging. Thus, novel anti‐bacteria Affitins which are cost‐effective, stable, and small can be rapidly and fully designed in vitro with high affinity and specificity for a surface‐exposed marker. This class of reagents can be useful in diagnostic and biomedical applications.
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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 | 0.000 |
| 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) | 0.000 | 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".