Integrating multiple experimental data to determine conformational ensembles of an intrinsically disordered protein
Bibliographic record
Abstract
Abstract Intrinsically disordered proteins (IDPs) have fluctuating heterogeneous conformations, which makes structural characterization challenging, but of great interest, since their conformational ensembles are the link between their sequences and functions. An accurate description of IDP conformational ensembles depends crucially on the amount and quality of the experimental data, how it is integrated, and if it supports a consistent structural picture. We have used an integrative modelling approach to understand how conformational restraints imposed by the most common structural techniques for IDPs: Nuclear Magnetic Resonance (NMR) spectroscopy, Small-angle X-ray Scattering (SAXS), and single-molecule Förster Resonance Energy Transfer (smFRET) reach concordance on structural ensembles for Sic1 and phosphorylated Sic1 (pSic1). To resolve apparent discrepancies between smFRET and SAXS, we integrated SAXS data with non-smFRET (NMR) data and reserved the new smFRET data for Sic1 and pSic1 as an independent validation. The consistency of the SAXS/NMR restrained ensembles with smFRET, which was not guaranteed a priori, indicates that the perturbative effects of NMR or smFRET labels on the Sic1 and pSic1 ensembles are minimal. Furthermore, the mutual agreement with such a diverse set of experimental data suggest that details of the generated ensembles can now be examined with a high degree of confidence to reveal distinguishing features of Sic1 vs. pSic1. From the experimentally well supported ensembles, we find they are consistent with independent biophysical models of Sic1’s ultrasensitive binding to its partner Cdc4. Our results underscore the importance of integrative modelling in calculating and drawing biological conclusions from IDP conformational ensembles.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".