Ensemble modeling of protein disordered states: Experimental restraint contributions and validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disordered states of proteins include the biologically functional intrinsically disordered proteins and the unfolded states of normally folded proteins. In recent years, ensemble-modeling strategies using various experimental measurements as restraints have emerged as powerful means for structurally characterizing disordered states. However, these methods are still in their infancy compared with the structural determination of folded proteins. Here, we have addressed several issues important to ensemble modeling using our ENSEMBLE methodology. First, we assessed how calculating ensembles containing different numbers of conformers affects their structural properties. We find that larger ensembles have very similar properties to smaller ensembles fit to the same experimental restraints, thus allowing a considerable speed improvement in our calculations. In addition, we analyzed the contributions of different experimental restraints to the structural properties of calculated ensembles, enabling us to make recommendations about the experimental measurements that should be made for optimal ensemble modeling. The effects of different restraints, most significantly from chemical shifts, paramagnetic relaxation enhancements and small-angle X-ray scattering, but also from other data, underscore the importance of utilizing multiple sources of experimental data. Finally, we validate our ENSEMBLE methodology using both cross-validation and synthetic experimental restraints calculated from simulated ensembles. Our results suggest that secondary structure and molecular size distribution can generally be modeled very accurately, whereas the accuracy of calculated tertiary structure is dependent on the number of distance restraints used.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it