Programing Chemical Communication: Allostery vs Multivalent Mechanism
Bibliographic record
Abstract
The emergence of life has relied on chemical communication and the ability to integrate multiple chemical inputs into a specific output. Two mechanisms are typically employed by nature to do so: allostery and multivalent activation. Although a better understanding of allostery has recently provided a variety of strategies to optimize the binding affinity, sensitivity, and specificity of molecular switches, mechanisms relying on multivalent activation remain poorly understood. As a proof of concept to compare the thermodynamic basis and design principles of both mechanisms, we have engineered a highly programmable DNA-based switch that can be triggered by either a multivalent or an allosteric DNA activator. By precisely designing the binding interface of the multivalent activator, we show that the affinity, dynamic range, and activated half-life of the molecular switch can be programed with even more versatility than when using an allosteric activator. The simplicity by which the activation properties of molecular switches can be rationally tuned using multivalent assembly suggests that it may find many applications in biosensing, drug delivery, synthetic biology, and molecular computation fields, where precise control over the transduction of binding events into a specific output is key.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".