A new Kunitz-type snake toxin family associated with an original mode of interaction with the vasopressin 2 receptor.
Bibliographic record
Abstract
Background and purpose. Venomous animals express numerous Kunitz-type peptides. The mambaquaretin-1 (MQ1) recently identified from the Dendroaspis angusticeps venom is the most selective antagonist of the arginine-vasopressin V2 receptor (V2R) and the unique Kunitz-type peptide active on a GPCR. We aimed to exploit other mamba venoms to enlarge the V2R-Kunitz peptide family and get insight into the MQ1 molecular mode of action. Experimental approach. We used a bio-guided screening assay to identify novel MQs and placed them phylogenetically. Several newly identified MQs were produced by solid phase peptide synthesis. They were characterized in vitro by binding and functional tests andin vivo by diuresis measurement in rats. Key results. Eight additional MQs were identified with nanomolar affinities for the V2R, all antagonists. MQs form a new subgroup in the Kunitz family, close to the V2R non-active dendrotoxins and to 2 V2R active cobra toxins. Sequence comparison between active and non-active V2R Kunitz peptides highlighted 5 specific V2R positions. Four of them are involved in V2R activity and belong to the 2 large MQ1 loops. We finally determined that 8 positions, part of these 2 loops, interact with the V2R. The variant MQ1-K39A showed specificity for the human versus the rat V2R . Conclusions and implications. A third function and mode of action is now associated with the Kunitz-peptides. The number of MQ1 residues involved in V2R binding is large and may explain its absolute selectivity. MQ1-K39A represents the first step in the improvement of the MQ1 design for medicinal perspective.
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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) | 0.001 | 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".