Predicting efficiency of NECEEM‐based partitioning of protein binders from nonbinders in DNA‐encoded libraries
Bibliographic record
Abstract
Nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) is an affinity method for separating binder-target complexes from nonbinders by gel-free CE. NECEEM is a promising high-efficiency method for partitioning protein binders from nonbinders in DNA-encoded libraries (DEL), such binders are used as "hits" in drug development. It is important to be able to predict the efficiency of NECEEM-based partitioning, which is the efficiency of collecting binders while removing nonbinders for a specific protein and a specific DEL with a minimum of empirical information. Here, we derive and study the dependence of efficiency of NECEEM-based partitioning on electrophoretic mobilities of the protein and the DNA moiety in DEL compounds. Our derivation is based upon a previously found relation between the electrophoretic mobility of protein-binder complex and measured electrophoretic mobilities of the protein and unbound DEL and their estimated sizes. The derivation utilizes the assumption of Gaussian shapes of electrophoretic peaks and the approximation of the efficiency of partitioning by the background of nonbinders - a fraction of nonbinders, which elutes along with protein-binder complexes. Our results will serve as a guiding tool for planning the NECEEM-based partitioning of protein binders from non-binders in DELs. In particular, it can be used to estimate a minimum number of rounds of partitioning required for the desired level of DEL enrichment.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".