Experimental and Computational Characterization of Disordered States of Proteins
Bibliographic record
Abstract
Disordered states of proteins include (i) the unfolded states of folded proteins and (ii) the biologically functional intrinsically disordered proteins. Due to the highly dynamic and conformationally heterogeneous nature of disordered states, traditional methods for structural characterization are not directly applicable. Nevertheless, recent years have brought major advances in the experimental characterization of disordered states. In particular, multidimensional NMR methods have proven extremely valuable for improving our understanding of these highly flexible systems. Extensive experimental evidence now supports the idea that disordered states under non-denaturing or mildly denaturing conditions have interesting structural properties that deviate substantially from the random coil-like behavior observed for chemically denatured proteins. In this chapter, we review various experimental techniques for characterizing non-random secondary and tertiary structure in disordered states of proteins. In addition, we discuss recent attempts at combining experimental measurements with computational methods in order to build detailed atomic-level models of various unfolded and intrinsically disordered proteins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".