Programming the Kinetics of Chemical Communication: Induced Fit vs Conformational Selection
Bibliographic record
Abstract
Life on Earth depends on chemical communication and the ability of biomolecular switches to integrate various chemical signals that trigger their activation or deactivation over time scales ranging from microseconds to days. The ability to similarly program and control the kinetics of artificial switches would greatly assist the design and optimization of future chemical and nanotechnological systems. Two distinct structure-switching mechanisms are typically employed by biomolecular switches: induced fit (IF) and conformational selection (CS). Despite 60 years of experimental and theoretical investigations, the kinetic and evolutive advantages of these two mechanisms remain unclear. Here, we have created a simple modular DNA switch that can operate through both mechanisms and be easily tuned and adapted to characterize its thermodynamic and kinetic parameters. We show that the fastest activation rate of a switch occurs when the ligand is able to bind its inactive conformation (IF). In contrast, we show that when the ligand can only bind the active conformation of the switch (CS), its activation rate can be easily programmed over many orders of magnitude by a simple tuning of its conformational equilibrium. We demonstrate the programming ability of both these mechanisms by designing a drug delivery vessel that can be programmed to release a drug over different time scales (>1000-fold). Overall, these findings provide a programmable strategy to optimize the kinetics of molecular systems and nanomachines while also illustrating how evolution may have taken advantage of IF and CS mechanisms to optimize the kinetics of biomolecular switches.
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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 | 0.001 |
| 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".