DNA Adsorption to the Reservoir Walls Causing Irreproducibility in Studies of Protein–DNA Interactions by Methods of Kinetic Capillary Electrophoresis
Bibliographic record
Abstract
Methods of kinetic capillary electrophoresis (KCE) facilitate kinetic studies of protein-DNA interactions and highly efficient selection of DNA aptamers for protein targets. Here, we report a previously unnoticed source of error that affects the precision and accuracy of KCE-based measurements. The error manifests itself in cases that require the use of low concentrations of DNA. In such measurements, the reproducibility of the signal generated by the same fluorescently labeled DNA sample can have a relative standard deviation (RSD) as high as 40%. We have investigated the cause of the irreproducibility and found that it is attributed to DNA adsorption to the surface of the sample vials, in which protein-DNA mixtures are prepared prior to a KCE experiment. The use of commercially available "high DNA recovery" sample vials does not resolve the problem. We have found that the problem can be significantly alleviated by the passivation of the vial surface with blocking agents, such as masking DNA or bovine serum albumin (BSA). The described adsorption of DNA to the surface of sample vials may also be important in other procedures that deal with low DNA concentrations, such as aptamer selection and quantitative PCR.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".