Rational design and selection of bivalent peptide ligands of thrombin incorporating P4-P1 tetrapeptide sequences: from good substrates to potent inhibitors
Bibliographic record
Abstract
The tetrapeptide Phe-Asn-Pro-Arg is a structurally optimized sequence for binding to the active site of thrombin. By conjugating this tetrapeptide or some variants to a C-terminal fragment of hirudin, we were able to generate a series of new bivalent inhibitors of thrombin containing only genetically encodable natural amino acids. We found that synergistic binding to both the active site and an exosite of thrombin can be enhanced through substitutions of amino acid residues at the P3 and P3' sites of the active-site directed sequence, Phe(P4)-Xaa(P3)-Pro(P2)-Arg(P1)-Pro(P1')-Gln(P2')-Yaa(P3'). Complementary to rational design, a phage library was constructed to explore further the residue requirements at the P4, P3 and P3' sites for bivalent and optimized two-site binding. Very significantly, panning of the phage library has led to thrombin-inhibitory peptides possessing strong anti-clotting activities in the low nanomolar range and yet interfering only partially the catalytic active site of thrombin. Modes of action of the newly discovered bivalent inhibitors are rationalized in light of the allosteric properties of thrombin, especially the interplay between the proteolytic action and regulatory binding occurring at thrombin surfaces remote from the catalytic active site.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".