Can Enzyme Engineering Benefit from the Modulation of Protein Motions? Lessons Learned from NMR Relaxation Dispersion Experiments
Bibliographic record
Abstract
Despite impressive progress in protein engineering and design, our ability to create new and efficient enzyme activities remains a laborious and time-consuming endeavor. In the past few years, intricate combinations of rational mutagenesis, directed evolution and computational methods have paved the way to exciting engineering examples and are now offering a new perspective on the structural requirements of enzyme activity. However, these structure-function analyses are usually guided by the time-averaged static models offered by enzyme crystal structures, which often fail to describe the functionally relevant ‘invisible states’ adopted by proteins in space and time. To alleviate such limitations, NMR relaxation dispersion experiments coupled to mutagenesis studies have recently been applied to the study of enzyme catalysis, effectively complementing ‘structure-function’ analyses with ‘flexibility-function’ investigation. In addition to offering quantitative, site-specific information to help characterize residue motion, these NMR methods are now being applied to enzyme engineering purposes, providing a powerful tool to help characterize the effects of controlling long-range networks of flexible residues affecting enzyme function. Recent advancements in this emerging field are presented here, with particular attention to mutagenesis reports highlighting the relevance of NMR relaxation dispersion tools in enzyme engineering. Keywords: CPMG, enzyme catalysis, NMR spectroscopy, protein engineering, relaxation dispersion, residue motion, Protein Motions, NMR Relaxation, rational mutagenesis, structure-function, flexibility-function, dispersion tools, biocatalysts, semi-random mutagenesis, de novo, nanoscale machines, dihydrofolate reductase, adenylate kinase, amino acid networks, enablers, disruptors, dispersion experiments, Cyclophilin A, Pin1, Ribonuclease A, drug development and nanotechnologyCPMG, enzyme catalysis, NMR spectroscopy, protein engineering, relaxation dispersion, residue motion, Protein Motions, NMR Relaxation, rational mutagenesis, structure-function, flexibility-function, dispersion tools, biocatalysts, semi-random mutagenesis, de novo, nanoscale machines, dihydrofolate reductase, adenylate kinase, amino acid networks, enablers, disruptors, dispersion experiments, Cyclophilin A, Pin1, Ribonuclease A, drug development and nanotechnology
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".