Selective Ion Binding and Uptake Shape the Microenvironment of Biomolecular Condensates
Bibliographic record
Abstract
Biomolecular condensates modulate various ion-dependent cellular processes and can regulate subcellular ion distributions by selective uptake of ions. To understand these processes, it is essential to uncover the molecular grammar governing condensate-ion interactions. Here, we use nuclear magnetic resonance (NMR) spectroscopy of ions and model condensate components to quantify and spatially resolve selective ion binding to condensates and show that these interactions follow the "law of matching water affinities", resulting in strong binding between proteins and chaotropic anions and between nucleic acids and kosmotropic cations. Ion uptake into condensates directly follows binding affinities, resulting in selective uptake of strong-binding ions but exclusion of weak-binding ions. Ion binding further shapes the condensate microenvironment by altering the composition, viscosity, and interface potential. Such changes can have profound effects on biochemical processes taking place inside condensates, as we show for RNA duplex formation. Our findings provide a new perspective on the role of condensate-ion interactions in cellular bio- and electrochemistry and may aid the design of condensate-targeting therapeutics.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".