Ideal‐filter capillary electrophoresis: A highly efficient partitioning method for selection of protein binders from oligonucleotide libraries
Bibliographic record
Abstract
Abstract Selection of affinity ligands for protein targets from oligonucleotide libraries currently involves multiple rounds of alternating steps of partitioning of protein‐bound oligonucleotides (binders) from protein‐unbound oligonucleotides (nonbinders). We have recently introduced ideal‐filter capillary electrophoresis (IFCE) for binder selection in a single step of partitioning. In IFCE, protein‐binder complexes and nonbinders move inside the capillary in the opposite directions, and the efficiency of their partitioning reaches 10 9 , i.e., only one of a billion molecules of nonbinders leaks through IFCE while all binders pass through. The condition of IFCE can be satisfied when the magnitude of the mobility of EOF is smaller than that of the protein‐binder complexes and larger than that of nonbinders. The efficiency of partitioning in IFCE is 10 million times higher than those of solid‐phase‐based methods of partitioning typically used in selection of affinity ligands for protein targets from oligonucleotide libraries. Here, we provide additional details on our justification for IFCE development. We elaborate on electrophoretic aspects of the method and define the theoretical range of EOF mobilities that support IFCE. Based on these theoretical results, we identify an experimental range of background electrolyte's ionic strength that supports IFCE. We also extend our interpretation of the results and discuss in‐depth IFCE's prospective in practical applications and fundamental 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 | 0.000 | 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".