DNase-Mediated Single-Cycle Selection of Aptamers for Proteins Blotted on a Membrane
Bibliographic record
Abstract
We describe a single-cycle DNA aptamer selection strategy that is able to obtain high affinity aptamers (K(d) of sub-nM) directly from a protein blotted on membrane. The key to the success of this strategy is the unique use of DNase I digestion to remove unwanted ssDNA from the membrane, leaving only the strongest bound aptamers. A crude Hepatitis B virus core protein (HBcAg) was separated using polyacrylamide gel electrophoresis (PAGE) and electro-blotted onto a polyvinylidene fluoride (PVDF) membrane. The membrane strip containing HBcAg and a second membrane strip containing human serum proteins were coincubated with a ssDNA library consisting of ∼10 copies each of 10(15) random sequences. Unbound and weakly bound sequences were efficiently removed from the membrane containing HBcAg using DNase I digestion and gradient wash with urea buffers. The remaining ssDNA bound to the target consisted of approximately 500 molecules, from which two aptamers with high affinity (K(d) ∼100 and 200 pM) were identified. This technique can be potentially used for selection of aptamers directly from multiple proteins that are separated by gel electrophoresis from a biological mixture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".