Mechanisms of Specific versus Nonspecific Interactions of Aggregation-Prone Inhibitors and Attenuators
Bibliographic record
Abstract
A common source of false positives in drug discovery is ligand self-association into large colloidal assemblies that nonspecifically inhibit target proteins. However, the mechanisms of aggregation-based inhibition (ABI) and ABI-attenuation by additives, such as Triton X-100 (TX) and human serum albumin (HSA), are not fully understood. Here, we investigate the molecular basis of ABI and ABI-attenuation through the lens of NMR and coupled thermodynamic cycles. We unexpectedly discover a new class of aggregating ligands that exhibit negligible interactions with proteins but act as competitive sinks for the free inhibitor, resulting in bell-shaped dose-response curves. TX attenuates ABI by converting inhibitory, protein-binding aggregates into nonbinding coaggregates, whereas HSA minimizes nonspecific ligand interactions by functioning as a reservoir for free inhibitor and preventing self-association. Hence, both TX and HSA are useful tools to minimize false positives arising from nonspecific binding but at the cost of potentially introducing false negatives due to suppression of specific 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".