A Novel Method to Measure Self-association of Small Amphipathic Molecules
Bibliographic record
Abstract
Biophysical techniques such as size-exclusion chromatography, sedimentation equilibrium analytical ultracentrifugation, and non-denaturing gel electrophoresis are the classical methods for determining the self-association of molecules into dimers, trimers, or other higher order species. However, these techniques usually require high (mg/ml) loading concentrations to detect self-association and also possess a lower size limit that is dependent on the ability of the technique to resolve monomeric from higher order species. Here we describe a novel, sensitive method with no upper or lower molecular size limits that indicates self-association of molecules driven together by the hydrophobic effect under aqueous conditions. "Temperature profiling in reversed-phase chromatography" analyzes the retention behavior of a sample over the temperature range of 5-80 degrees C during gradient elution reversed-phase high-performance liquid chromatography. Because this technique greatly increases the effective concentration of analyte upon adsorption to the column, it is extremely sensitive, requiring very small sample quantities (microgram or less). In contrast, the classical techniques mentioned above decrease the effective analyte concentration during analysis, decreasing sensitivity by requiring larger amounts of analyte to detect molecular self-association. We demonstrate the utility of this technique with 14-residue cyclic and linear cationic peptides (<2000 Da) based on the sequence of the de novo-designed cytolytic peptide, GS14. The only requirements for the analyte molecule when using this technique are its ability to be retained on the reversed-phase column and to be subsequently removed from the column during gradient elution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".