Troubleshooting Guide to Expressing Intrinsically Disordered Proteins for Use in NMR Experiments
Bibliographic record
Abstract
Intrinsically disordered proteins (IDPs) represent a structural class of proteins that do not have a well-defined, 3D fold in solution, and often have little secondary structure. To characterize their function and molecular mechanism, it is helpful to examine their structure using nuclear magnetic resonance (NMR), which can report on properties such as residual structure (at both the secondary and tertiary levels), ligand binding affinity, and the effect of ligand binding on IDP structure. This brief review reports on the common problems and decisions that are involved when preparing a disordered protein for NMR studies. The paper covers gene design, expression host choice, protein purification, and the initial NMR experiments that are performed. While many of these steps are essentially identical to those used for ordered proteins, there are several, key differences, including the need for protein labeling with stable isotopes, the extreme sensitivity of IDPs to proteolytic cleavage, and the ability to use denaturing conditions without having to refold the protein. After successful purification, characterization by NMR can be done using the standard 15N-heteronuclear single quantum coherence (15N-HSQC) experiment, or the newer CON series of experiments that have been optimized for 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.108 |
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".