Single-molecule force spectroscopy reveals a mechanically stable protein fold and the rational tuning of its mechanical stability
Bibliographic record
Abstract
It is recognized that shear topology of two directly connected force-bearing terminal beta-strands is a common feature among the vast majority of mechanically stable proteins known so far. However, these proteins belong to only two distinct protein folds, Ig-like beta sandwich fold and beta-grasp fold, significantly hindering delineating molecular determinants of mechanical stability and rational tuning of mechanical properties. Here we combine single-molecule atomic force microscopy and steered molecular dynamics simulation to reveal that the de novo designed Top7 fold [Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D (2003) Science 302:1364-1368] represents a mechanically stable protein fold that is distinct from Ig-like beta sandwich and beta-grasp folds. Although the two force-bearing beta strands of Top7 are not directly connected, Top7 displays significant mechanical stability, demonstrating that the direct connectivity of force-bearing beta strands in shear topology is not mandatory for mechanical stability. This finding broadens our understanding of the design of mechanically stable proteins and expands the protein fold space where mechanically stable proteins can be screened. Moreover, our results revealed a substructure-sliding mechanism for the mechanical unfolding of Top7 and the existence of two possible unfolding pathways with different height of energy barrier. Such insights enabled us to rationally tune the mechanical stability of Top7 by redesigning its mechanical unfolding pathway. Our study demonstrates that computational biology methods (including de novo design) offer great potential for designing proteins of defined topology to achieve significant and tunable mechanical properties in a rational and systematic fashion.
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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) | 0.000 | 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".