CryoEM reveals unprecedented binding site for Na <sub>V</sub> 1.7 inhibitors enabling rational design of potent hybrid inhibitors
Bibliographic record
Abstract
Abstract The voltage-gated sodium (NaV) channel NaV1.7 has been identified as a potential novel pain target due to its striking human genetics. However, clinically available drugs (e.g. lidocaine, carbamazepine, etc.) are not selective among the nine NaV channel subtypes, NaV1.1-NaV1.9, and the two currently known classes of NaV1.7 subtype-selective inhibitors (aryl- and acylsulfonamides) have undesirable characteristics that may limit their development. Moreover, understanding of the structure-activity relationships of the acylsulfonamide class of NaV1.7 inhibitors, exemplified by the clinical development candidate GDC-0310 , has been based solely on a single co-crystal structure of an arylsulfonamide inhibitor series. To advance inhibitor design targeting the NaV1.7 channel, we established an iterative system to routinely obtain high-resolution ligand-bound NaV1.7 structures using cryogenic electron microscopy (cryo-EM). We report that GDC-0310 engages the NaV1.7 voltage-sensing domain 4 (VSD4) through an unexpected binding mode orthogonal to the arylsulfonamide class binding pose, which identifies a previously unknown ligand binding site in NaV channels. This finding enabled the design of a novel hybrid inhibitor series that bridges the aryl and acylsulfonamide binding pockets and allows for the generation of molecules with substantially differentiated structures and properties. Overall, this study highlights the power of cryo-EM methods to pursue challenging drug targets using iterative and high-resolution structure-guided inhibitor design. It also underscores an important role of the membrane bilayer in the discovery of selective NaV channel modulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".