Identification of an alpha-1 antitrypsin variant with enhanced specificity for factor XIa by phage display, bacterial expression, and combinatorial mutagenesis
Bibliographic record
Abstract
Coagulation Factor XIa (FXIa) is an emerging target for antithrombotic agent development. The M358R variant of the serpin alpha-1 antitrypsin (AAT) inhibits both FXIa and other proteases. Our aim was to enhance the specificity of AAT M358R for FXIa. We randomized two AAT M358R phage display libraries at reactive centre loop positions P13-P8 and P7-P3 and biopanned them with FXIa. A bacterial expression library randomized at P2'-P3' was also probed. Resulting novel variants were expressed as recombinant proteins in E. coli and their kinetics of FXIa inhibition determined. The most potent FXIa-inhibitory motifs were: P13-P8, HASTGQ; P7-P3, CLEVE; and P2-P3', PRSTE (respectively, novel residues bolded). Selectivity for FXIa over thrombin was increased up to 34-fold versus AAT M358R for these single motif variants. Combining CLEVE and PRSTE motifs in AAT-RC increased FXIa selectivity for thrombin, factors XIIa, Xa, activated protein C, and kallikrein by 279-, 143-, 63-, 58-, and 36-fold, respectively, versus AAT M358R. AAT-RC lengthened human plasma clotting times less than AAT M358R. AAT-RC rapidly and selectively inhibits FXIa and is worthy of testing in vivo. AAT specificity can be focused on one target protease by selection in phage and bacterial systems coupled with combinatorial mutagenesis.
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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".