Multiscale computational analysis reveals enhanced allosteric modulation of Nav1.5 by dual binding of dapagliflozin and ertugliflozin
Bibliographic record
Abstract
The cardiac voltage-gated sodium ion channel (Nav1.5) serves as a crucial regulator of cardiac excitability and presents a potential therapeutic target. While the Sodium-Glucose Cotransporter 2 (SGLT2) inhibitor dapagliflozin has exhibited cardioprotective effects, its structurally similar counterpart, ertugliflozin, which differs solely by an additional oxygen and methyl group, does not confer cardio-protection. To examine this discrepancy, a multiscale computational approach combining all-atom molecular dynamics (MD) and coarse-grained (CG) simulations was employed to analyze ligand interactions with Nav1.5 in both single and dual-binding site configurations. All-atom simulations revealed localized residue fluctuations but were inadequate for capturing system-wide allosteric effects. Consequently, CG models were derived from atomistic trajectories to improve conformational sampling. Critical residues regulating binding sites were identified through B-factor analyses in three replicas of all-atom models. Harmonic restraints were subsequently applied to these residues within the CG models to simulate ligand-induced rigidity. Notably, in the dual-binding configurations, ertugliflozin’s additional oxygen established a hydrogen bond interaction with Y1767, a mutation site associated with pathological late sodium current (late INa). This interaction was absent in single-site configurations and may elucidate the functional divergence between the two compounds. Furthermore, inter-residue distances between the IFMT motif, involving domains III and IV related to channel gating, were monitored to assess inactivation states across the various systems. This investigation underscores how dual-site occupancy and subtle chemical differences in ligands can impact Nav1.5 inactivation dynamics. The findings provide a foundation for structure-based design of selective modulators targeting sodium channels via allosteric mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".