Faculty Opinions recommendation of Mechanistic dissection of increased enzymatic rate in a phase-separated compartment.
Bibliographic record
Abstract
Biomolecular condensates concentrate macromolecules into discrete cellular foci without an encapsulating membrane. Condensates are often presumed to increase enzymatic reaction rates through increased concentrations of enzymes and substrates (mass action), although this idea has not been widely tested and other mechanisms of modulation are possible. Here we describe a synthetic system where the SUMOylation enzyme cascade is recruited into engineered condensates generated by liquid-liquid phase separation of multidomain scaffolding proteins. SUMOylation rates can be increased up to 36-fold in these droplets compared to the surrounding bulk, depending on substrate KM. This dependency produces substantial specificity among different substrates. Analyses of reactions above and below the phase-separation threshold lead to a quantitative model in which reactions in condensates are accelerated by mass action and changes in substrate KM, probaby due to scaffold-induced molecular organization. Thus, condensates can modulate reaction rates both by concentrating molecules and physically organizing them. PMID: 34035521 Funding information This work was supported by: Howard Hughes Medical Institute, United States
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.059 |
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".