Selection of aptamers for a protein target in cell lysate and their application to protein purification
Bibliographic record
Abstract
Functional genomics requires structural and functional studies of a large number of proteins. While the production of proteins through over-expression in cultured cells is a relatively routine procedure, the subsequent protein purification from the cell lysate often represents a significant challenge. The most direct way of protein purification from a cell lysate is affinity purification using an affinity probe to the target protein. It is extremely difficult to develop antibodies, classical affinity probes, for a protein in the cell lysate; their development requires a pure protein. Thus, isolating the protein from the cell lysate requires antibodies, while developing antibodies requires a pure protein. Here we resolve this loop problem. We introduce AptaPIC, Aptamer-facilitated Protein Isolation from Cells, a technology that integrates (i) the development of aptamers for a protein in cell lysate and (ii) the utilization of the developed aptamers for protein isolation from the cell lysate. Using MutS protein as a target, we demonstrate that this technology is applicable to the target protein being at an expression level as low as 0.8% of the total protein in the lysate. AptaPIC has the potential to considerably speed up the purification of proteins and, thus, accelerate their structural and functional studies.
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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 | 0.001 | 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".