Effect of multiple symmetries on the association of R67 DHFR subunits bearing interfacial complementing mutations
Bibliographic record
Abstract
It was shown previously that complementation could be a powerful mean to probe protein-protein interactions in the normally tetrameric R67 DHFR. Indeed, mixing complementing inactive dimeric mutants produced active heterotetramers. This approach turned a homo-oligomer into a hetero-oligomer and thus allowed the use of combinatorial assays, a subtle analysis of the association forces, and a precise determination of the equilibrium dissociation constants (K(D)) by titrimetry. However, for some of the complementing pairs, the experimental data implied multiple equilibria involving heterodimers, although no monomers could be detected. Thus, the reactions involved had to be identified to elaborate a suitable model to determine the K(D) of those pairs correctly. That model suggested that homodimers associated rapidly before the protomers could be redistributed in a multiple equilibrium system. Kinetic data confirmed that view. The association data at equilibrium were analyzed by multiple curve fitting with all plausible combinations of parameters. This gave a confidence interval for K(D) that is safer than the usual 67% or 90% confidence interval. Finally, the K(D) of one specific reaction, the dissociation of a heterotetramer with the relevant symmetry into two homodimers could be determined with the relevant model for each complementing pair, although multiple equilibria were present. These K(D) can thus be used as a set of references data to test and improve theoretical methods such as association free energy calculations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".