Structural and Functional Diversity of Animal Toxins Interacting With GPCRs
Bibliographic record
Abstract
Peptide toxins from venoms have undergone a long evolutionary process allowing host defense or prey capture and making them highly selective and potent for their target. This has resulted in the emergence of a large panel of toxins from a wide diversity of species, with varied structures and multiple associated biological functions. In this way, animal toxins constitute an inexhaustible reservoir of druggable molecules due to their interesting pharmacological properties. One of the most interesting classes of therapeutic targets is the G-protein coupled receptors (GPCRs). GPCRs represent the largest family of membrane receptors in mammals with approximately 800 different members. They are involved in almost all biological functions and are the target of almost 30% of drugs currently on the market. Given the interest of GPCRs in the therapeutic field, the study of toxins that can interact with and modulate their activity with the purpose of drug development is of particular importance. The present review focuses on toxins targeting GPCRs, including peptide-interacting receptors or aminergic receptors, with a particular focus on structural aspects and, when relevant, on potential medical applications. The toxins described here exhibit a great diversity in size, from 10 to 80 amino acids long, in disulfide bridges, from none to five, and belong to a large panel of structural scaffolds. Particular toxin structures developed here include inhibitory cystine knot (ICK), three-finger fold, and Kunitz-type toxins. We summarize current knowledge on the structural and functional diversity of toxins interacting with GPCRs, concerning first the agonist-mimicking toxins that act as endogenous agonists targeting the corresponding receptor, and second the toxins that differ structurally from natural agonists and which display agonist, antagonist, or allosteric properties.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".