Decoupling a tandem-repeat protein: Impact of multiple loop insertions on a modular scaffold
Bibliographic record
Abstract
The simple topology and modular architecture of tandem-repeat proteins such as tetratricopeptide repeats (TPRs) and ankyrin repeats makes them straightforward to dissect and redesign. Repeat-protein stability can be manipulated in a predictable way using site-specific mutations. Here we explore a different type of modification - loop insertion - that will enable a simple route to functionalisation of this versatile scaffold. We previously showed that a single loop insertion has a dramatically different effect on stability depending on its location in the repeat array. Here we dissect this effect by a combination of multiple and alternated loop insertions to understand the origins of the context-dependent loss in stability. We find that the scaffold is remarkably robust in that its overall structure is maintained. However, adjacent repeats are now only weakly coupled, and consequently the increase in solvent protection, and thus stability, with increasing repeat number that defines the tandem-repeat protein class is lost. Our results also provide us with a rulebook with which we can apply these principles to the design of artificial repeat proteins with precisely tuned folding landscapes and functional capabilities, thereby paving the way for their exploitation as a versatile and truly modular platform in synthetic biology.
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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 | 0.000 | 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) | 0.001 | 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".