Compatibility of intracellular binding: Evolutionary design principles for metal sensors
Bibliographic record
Abstract
In the common cellular space, hundreds of binding reactions occur reliably and simultaneously without disruptive mutual interference. The design principles that enable this remarkable compatibility have not yet been adequately elucidated. In order to delineate these principles, we consider the intracellular sensing of transition metals in bacteria-an integral part of cellular metal homeostasis. Protein cytosolic sensors typically interact with metals through three types of lateral chain residues, containing oxygen, nitrogen, or sulfur. The very existence of complete sets of mutually compatible sensors is a nontrivial problem solved by evolution, since each metal sensor has to bind to its cognate metal without being "mismetallated" by noncognate competitors. Here, based solely on theoretical considerations and limited information about binding constants for metal-amino acid interactions, we are able to predict possible "sensor compositions," i.e., the residues forming the binding sites. We find that complete transition-metal sensor sets are severely limited in their number by compatibility requirements, leaving only a handful of possible sensor compositions for each transition metal. Our theoretical results turn out to be broadly consistent with experimental data on known bacterial sensors. If applicable to other cytosolic binding interactions, the results generated by our approach imply that compatibility requirements may play a crucial role in the organization and functioning of intracellular processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".