Empirical predictor of conditions that support ideal‐filter capillary electrophoresis
Bibliographic record
Abstract
Ideal-filter CE (IFCE) is a method for the selection of affinity binders for protein targets from oligonucleotide libraries, for example, random-sequence oligonucleotide libraries and DNA-encoded libraries, in a single step of partitioning. In IFCE, protein-oligonucleotide complexes and unbound oligonucleotides move in the opposite directions, facilitating very high efficiency of their partitioning. For any given protein target and oligonucleotide library, protein-oligonucleotide complexes and unbound oligonucleotides move in the opposite directions only for a limited range of EOF mobilities, which, in turn, corresponds to a limited range of pH and ionic strength values of the running buffer. Rational design of IFCE-based partitioning requires a priori knowledge of this range of pH and ionic strength values, and here we introduce an approach to predict this range for a given type of the running buffer. The approach involves measuring EOF mobilities for a relatively wide range of pH and ionic strength (I) values and finding an empirical predictor function that related the EOF mobility with pH and ionic strength. In this work, we developed a predictor function for a running buffer (Tris-HCl) that is commonly used in CE-based partitioning of affinity binders for protein targets. This predictor function can be immediately used for the rational design of IFCE-based partitioning in this running buffer, while the described approach will be used to develop predictor functions for other types of running buffer if needed.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".