Using DNA-Binding Proteins as an Analytical Tool
Bibliographic record
Abstract
We propose that DNA-binding proteins can be used as highly efficient and versatile tools in analyses of DNA, RNA, and proteins. This work reports assays applying specific affinity probes: hybridization probes for analyses of DNA and RNA, and aptamer probes for analyses of proteins. Both types of probes are single-stranded DNA. In affinity analyses, in general, the probe (P) binds to a target molecule (T), and the amounts of the probe-target complex (P.T) and unbound P are determined. Distinguishing between P and P.T can be achieved by electrophoretic separation. If the electrophoretic mobilities of P and P.T are close in gel-free media, which is always the case for hybridization analyses, separation typically requires the use of a sieving matrix. Here we utilized a single-stranded DNA binding protein (SSB) to facilitate highly efficient gel-free separation of P and P.T in capillary electrophoresis (CE) for three types of targets: DNA, RNA, and proteins. When present in the CE run buffer, SSB binds differently to P and P.T. Due to this selective binding, SSB induces difference in electrophoretic mobilities of P and P.T in an SSB concentration-dependent fashion. The difference in the electrophoretic mobilities allows for affinity analyses of DNA, RNA, and proteins in gel-free CE. The large number of well-characterized DNA- and RNA-binding proteins and the diversity of their properties will allow researchers to design a comprehensive tool set for quantitative analyses of DNA, RNA, and proteins. Such analyses will facilitate identification of genomic DNA in ultra-small samples without error-prone and time-consuming PCR. They can also be used for monitoring gene expression at both mRNA and protein levels.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".