A Fast-acting, Modular-structured Staphylokinase Fusion with Kringle-1 from Human Plasminogen as the Fibrin-targeting Domain Offers Improved Clot Lysis Efficacy
Bibliographic record
Abstract
To develop a fast-acting clot dissolving agent, a clot-targeting domain derived from the Kringle-1 domain in human plasminogen was fused to the C-terminal end of staphylokinase with a linker sequence in between. Production of this fusion protein in Bacillus subtilis and Pichia pastoris was examined. The Kringle domain in the fusion protein produced from B. subtilis was improperly folded because of its complicated disulfide-bond profile, whereas the staphylokinase domain produced from P. pastoris was only partially active because of an N-linked glycosylation. A change of the glycosylation residue, Thr-30, to alanine resulted in a non-glycosylated biologically active fusion. The resulting mutein, designated SAKM3-L-K1, was overproduced in P. pastoris. Each domain in SAKM3-L-K1 was functional, and this fusion showed fibrin binding ability by binding directly to plasmin-digested clots. In vitro fibrin clot lysis in a static environment and plasma clot lysis in a flow-cell system demonstrated that the engineered fusion outperformed the non-fused staphylokinase. The time required for 50% clot lysis was reduced by 20 to 500% under different conditions. Faster clot lysis can potentially reduce the degree of damage to occluded heart tissues.
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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".