Recombination of protein fragments: A promising approach toward engineering proteins with novel nanomechanical properties
Bibliographic record
Abstract
Combining single molecule atomic force microscopy (AFM) and protein engineering techniques, here we demonstrate that we can use recombination-based techniques to engineer novel elastomeric proteins by recombining protein fragments from structurally homologous parent proteins. Using I27 and I32 domains from the muscle protein titin as parent template proteins, we systematically shuffled the secondary structural elements of the two parent proteins and engineered 13 hybrid daughter proteins. Although I27 and I32 are highly homologous, and homology modeling predicted that the hybrid daughter proteins fold into structures that are similar to that of parent protein, we found that only eight of the 13 daughter proteins showed beta-sheet dominated structures that are similar to parent proteins, and the other five recombined proteins showed signatures of the formation of significant alpha-helical or random coil-like structure. Single molecule AFM revealed that six recombined daughter proteins are mechanically stable and exhibit mechanical properties that are different from the parent proteins. In contrast, another four of the hybrid proteins were found to be mechanically labile and unfold at forces that are lower than the approximately 20 pN, as we could not detect any unfolding force peaks. The last three hybrid proteins showed interesting duality in their mechanical unfolding behaviors. These results demonstrate the great potential of using recombination-based approaches to engineer novel elastomeric protein domains of diverse mechanical properties. Moreover, our results also revealed the challenges and complexity of developing a recombination-based approach into a laboratory-based directed evolution approach to engineer novel elastomeric proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".