Changes in Potency and Subtype Selectivity of Bivalent Na<sub>V</sub> Toxins are Knot-Specific
Bibliographic record
Abstract
Disulfide-rich peptide toxins have long been studied for their ability to inhibit voltage-gated sodium channel subtype Na V 1.7, a validated target for the treatment of pain. In this study, we sought to combine the pore blocking activity of conotoxins with the gating modifier activity of spider toxins to design new bivalent inhibitors of Na V 1.7 with improved potency and selectivity. To do this, we created an array of heterodimeric toxins designed to target human Na V 1.7 by ligating a conotoxin to a spider toxin and assessed the potency and selectivity of the resulting bivalent toxins. A series of spider-derived gating modifier toxins (GpTx-1, ProTx-II, gHwTx-IV, JzTx-V, CcoTx-1, and Pn3a) and two pore-blocker μ-conotoxins, SxIIIC and KIIIA, were used for this study. We employed either enzymatic ligation with sortase A for C- to N-terminal ligation or click chemistry for N- to N-terminal ligation. The bivalent peptide resulting from ligation of ProTx-II and SxIIIC (Pro[LPATG 6 ]Sx) was shown to be the best combination as native ProTx-II potency at hNa V 1.7 was conserved following ligation. At hNa V 1.4, a synergistic effect between the pore blocker and gating modifier toxin moieties was observed, resulting in altered sodium channel subtype selectivity compared to the parent peptides. Further studies including mutant bivalent peptides and mutant hNa V 1.7 channels suggested that gating modifier toxins have a greater contribution to the potency of the bivalent peptides than pore blockers. This study delineated potential benefits and drawbacks of designing pharmacological hybrid peptides targeting hNa V 1.7.
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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 | 0.001 |
| 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".