Is Buffer a Good Proxy for a Crowded Cell-Like Environment? A Comparative NMR Study of Calmodulin Side-Chain Dynamics in Buffer and E. coli Lysate
Bibliographic record
Abstract
Biophysical studies of protein structure and dynamics are typically performed in a highly controlled manner involving only the protein(s) of interest. Comparatively fewer such studies have been carried out in the context of a cellular environment that typically involves many biomolecules, ions and metabolites. Recently, solution NMR spectroscopy, focusing primarily on backbone amide groups as reporters, has emerged as a powerful technique for investigating protein structure and dynamics in vivo and in crowded "cell-like" environments. Here we extend these studies through a comparative analysis of Ile, Leu, Val and Met methyl side-chain motions in apo, Ca(2+)-bound and Ca(2+), peptide-bound calmodulin dissolved in aqueous buffer or in E. coli lysate. Deuterium spin relaxation experiments, sensitive to pico- to nano-second time-scale processes and Carr-Purcell-Meiboom-Gill relaxation dispersion experiments, reporting on millisecond dynamics, have been recorded. Both similarities and differences in motional properties are noted for calmodulin dissolved in buffer or in lysate. These results emphasize that while significant insights can be obtained through detailed "test-tube" studies, experiments performed under conditions that are "cell-like" are critical for obtaining a comprehensive understanding of protein motion in vivo and therefore for elucidating the relation between motion and function.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".