Volatile Kinetic Capillary Electrophoresis for Studies of Protein–Small Molecule Interactions
Bibliographic record
Abstract
Kinetic capillary electrophoresis (KCE) is a toolset of homogeneous affinity methods for studying kinetics of noncovalent binding. Sensitive KCE measurements are typically done with fluorescence detection and require a fluorescent label on a smaller-sized binding partner. KCE with fluorescence detection is difficult to use for study of protein-small molecule interactions since labeling small molecules is cumbersome and can affect binding. A combination of KCE with mass-spectrometry (KCE-MS) has been recently suggested for label-free studies of protein-small molecule interactions. The major obstacle for studies by KCE-MS is a buffer mismatch between KCE and MS; MS requires volatile buffers while KCE of protein-ligand interactions is always run in near-physiological buffers. Here we asked a simple question: can protein-ligand interactions be studied with KCE in a volatile buffer? We compared three volatile buffers (ammonium acetate, ammonium bicarbonate, and ammonium formate) with a near-physiological buffer (Tris-acetate) for three protein-ligand pairs. The volatile buffers were found not to significantly affect protein-ligand complex stability; moreover, when used as CE run buffers, they facilitated good-quality separation of free ligands from the protein-ligand complexes. The use of volatile buffers instead of Tris-acetate in detection of small molecules by MS improved the detection limit by approximately 2 orders of magnitude. These findings prove the principle of "volatile" KCE, which can be easily coupled with MS to facilitate label-free kinetic studies of protein-small molecule interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".