Mapping Functional Dynamics Hotspots for Protein Engineering with NMR Peak Intensity Analysis
Bibliographic record
Abstract
Structural dynamics play a crucial role in protein function, and tuning these dynamics through mutagenesis has emerged as a promising strategy for enhancing activity. However, identifying dynamics hotspots for protein engineering remains a labor-intensive challenge. Here, we demonstrate that NMR peak intensity analysis-a rapid, qualitative method with residue-level resolution-can identify functionally relevant dynamic regions with high precision. Using a family of red fluorescent proteins (RFPs) as a case study, we reveal that flexibility in specific regions of their structures correlates with function. Specifically, as quantum yield increases, the side of the β-barrel closest to the chromophore phenolate moiety becomes more rigid, while the opposite side, closest to the acylimine group, gains flexibility. Notably, the phenolate face corresponds to a mutational hotspot frequently targeted in directed evolution campaigns aimed at enhancing brightness, underscoring its functional significance. B-factor analysis of non-cryogenic X-ray crystal structures further supports our findings. Our results establish NMR peak intensity analysis as a promising tool for mapping functional dynamics hotspots to guide protein engineering campaigns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".