Bio-Layer Interferometry Analysis of the Target Binding Activity of CRISPR-Cas Effector Complexes
Bibliographic record
Abstract
CRISPR-Cas systems employ ribonucleoprotein complexes to identify nucleic acid targets with complementarity to bound CRISPR RNAs. Analyses of the high diversification of these effector complexes suggest that they can exhibit a wide spectrum of target requirements and binding affinities. Therefore, streamlined analysis techniques to study the interactions between nucleic acids and proteins are necessary to facilitate the characterization and comparison of CRISPR-Cas effector activities. Bio-layer Interferometry (BLI) is a technique that measures the interference pattern of white light that is reflected from a layer of biomolecules immobilized on the surface of a sensor tip (bio-layers) in real time and in solution. As streptavidin-coated sensors and biotinylated oligonucleotides are commercially available, this method enables straightforward measurements of the interaction of CRISPR-Cas complexes with different targets in a qualitative and quantitative fashion. Here, we present a general method to carry out binding assays with the Type I-Fv complex from Shewanella putrefaciens and the Type I-F complex from Shewanella baltica as model effectors. We report target specificities, dissociation constants and interactions with the Anti-CRISPR protein Acr7 to highlight possible applications of this technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".