Internal Structure and Preferential Protein Binding of Colloidal Aggregates
Bibliographic record
Abstract
Colloidal aggregates of small molecules are the most common artifact in early drug discovery, sequestering and inhibiting target proteins without specificity. Understanding their structure and mechanism has been crucial to developing tools to control for, and occasionally even exploit, these particles. Unfortunately, their polydispersity and transient stability have prevented exploration of certain elementary properties, such as how they pack. Dye-stabilized colloidal aggregates exhibit enhanced homogeneity and stability when compared to conventional colloidal aggregates, enabling investigation of some of these properties. By small-angle X-ray scattering and multiangle light scattering, pair distance distribution functions suggest that the dye-stabilized colloids are filled, not hollow, spheres. Stability of the coformulated colloids enabled investigation of their preference for binding DNA, peptides, or folded proteins, and their ability to purify one from the other. The coformulated colloids showed little ability to bind DNA. Correspondingly, the colloids preferentially sequestered protein from even a 1600-fold excess of peptides that are themselves the result of a digest of the same protein. This may reflect the avidity advantage that a protein has in a surface-to-surface interaction with the colloids. For the first time, colloids could be shown to have preferences of up to 90-fold for particular proteins over others. Loaded onto the colloids, bound enzyme could be spun down, resuspended, and released back into buffer, regaining most of its activity. Implications of these observations for colloid mechanisms and utility will be considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".