Accurate Kd via Transient Incomplete Separation
Bibliographic record
Abstract
Current methods for finding the equilibrium dissociation constant, K d , of protein-small molecule complexes have inherent sources of inaccuracy. We introduce “Accurate K d via Transient Incomplete Separation” (AKTIS), an approach that is free of known sources of inaccuracy. Conceptually, in AKTIS, a short plug of the pre-equilibrated protein-small molecule mixture is pressure-propagated in a capillary, causing transient incomplete separation of the complex from the unbound small molecule. A superposition of signals from these two components is measured near the capillary exit as a function of time, for different concentrations of the protein and a constant concentration of the small molecule. Finally, a classical binding isotherm is built and used to find accurate K d value. Here we prove AKTIS validity theoretically and by computer simulation, present a fluidic system satisfying AKTIS requirements, and demonstrate practical application of AKTIS to finding K d of protein-small molecule complexes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".