Molecular Crowding Stabilizes Both the Intrinsically Disordered Calcium-Free State and the Folded Calcium-Bound State of a Repeat in Toxin (RTX) Protein
Bibliographic record
Abstract
Macromolecular crowding affects most chemical equilibria in living cells, as the presence of high concentrations of macromolecules sterically restricts the available space. Here, we characterized the influence of crowding on a prototypical RTX protein, RC(L). RTX (Repeat in ToXin) motifs are calcium-binding nonapeptide sequences that are found in many virulence factors produced by Gram-negative bacteria and secreted by dedicated type 1 secretion systems. RC(L) is an attractive model to investigate the effect of molecular crowding on ligand-induced protein folding, as it shifts from intrinsically disordered conformations (apo-form) to a stable structure upon calcium binding (holo-form). It thus offers the rare opportunity to characterize the crowding effects on the same polypeptide chain under two drastically distinct folding states. We showed that the crowding agent Ficoll70 did not affect the structural content of the apo-state and holo-state of RC(L) but increased the protein affinity for calcium. Moreover, Ficoll70 strongly stabilized both states of RC(L), increasing their half-melting temperature, without affecting enthalpy changes. The power law dependence of the melting temperature increase (ΔT(m)) on the volume fraction (φ) followed theoretical excluded volume predictions and allowed the estimation of the Flory exponent (ν) of the thermally unfolded polypeptide chain in both states. Altogether, our data suggest that, in the apo-state as found in the crowded bacterial cytosol, RTX proteins adopt extended unfolded conformations that may facilitate protein export by the type I secretion machinery. Subsequently, crowding also enhances the calcium-dependent folding and stability of RTX proteins once secreted in the extracellular milieu.
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 | 0.000 |
| 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 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".