Mapping Charge Interactions in Intrinsically Disordered Proteins
Bibliographic record
Abstract
Intrinsically disordered proteins (IDPs) are often rich in charged residues, and electrostatic interactions have a pronounced effect on their conformational distributions, interactions and functions. However, attaining quantitative understanding of electrostatics is challenging because of the sequence-specific arrangement of charges in the chain, the long-range nature of electrostatic interactions, charge screening, and the condensation of counterions-effects that all need to be taken into account self-consistently. Here, analytically tractable quantitative models are developed to predict ensemble average distances between any pair of residues in IDPs as a function of sequence and salt concentration, explicitly considering charge patterning. These models are tested systematically against extensive single-molecule Förster resonance energy transfer (FRET) data mapping intrachain distances for a range of charged IDPs with different sequence compositions, as a function of salt concentration, and with different labeling positions and fluorophores. The resulting polymer model with a minimal set of adjustable parameters accounts for counterion condensation, the resulting effective charges, as well as dipolar interactions, and can be used to predict detailed intrachain distance maps between all residues. Analytical models of this kind offer a valuable complement to simulations and can provide fundamental insight into the interactions underlying the conformational distributions of IDPs.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".