Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape
Bibliographic record
Abstract
ABSTRACT The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants may render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. The effects of single amino acid substitutions on PAI-1’s functional inhibition of its canonical target proteases, tPA and uPA, have been determined for only a small fraction of potential mutations. To construct a more comprehensive dataset, a mutagenized PAI-1 library, encompassing ∼70% of potential single amino acid substitutions, was displayed on M13 filamentous phage. From this library, the relative effects of 27% of all possible missense variants on PAI-1 inhibition of urokinase-type plasminogen activator were determined using high-throughput DNA sequencing with 826 missense variants demonstrating conserved inhibitory activity and 1137 resulting in loss of PAI-1 function. Comparison of these deep mutational scanning results to predictions from PolyPhen-2 and SIFT demonstrate the limitations of these algorithms, consistent with similar reports for other proteins. Comparison to common human PAI-1 gene variants present in the gnomAD database is consistent with evolutionary selection against loss of PAI-1 function. These findings provide insight into structure-function relationships for PAI-1 and other members of the SERPIN superfamily.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".