Modeling TRPV-like Receptors in C. elegans: Structural Insights into OSM-9 and OCR-2 Role in Nociception and Vanilloid Ligand Interactions
Bibliographic record
Abstract
Chronic pain is one of the most debilitating conditions affecting large sections of the human population worldwide. Novel approaches to treat this condition are needed to provide physicians with alternatives to rising diagnostics and the abuse of opioid-based treatments. Transient receptor potential vanilloid 1 (TRPV1) plays a pivotal role in mediating nociceptive stimuli in mammals. It is activated by vanilloid compounds such as capsaicin, which leads pain relief through receptor desensitization. In this study, we used molecular dynamics to characterize the molecular interactions that mediate previously observed capsaicin-mediated nocifensive responses in Caenorhabditis elegans. Specifically, we modeled homo- and heterotetrameric structures for TRPV1, OSM-9, and OCR-2 using AlphaFold3. We then used flexible receptor docking to find capsaicin binding poses for all the modeled receptors. These poses were further analyzed through molecular dynamics simulations to determine stability, key residues, trajectory, and free energy landscapes. The results indicated that ligand-receptor interactions are dominated by Van der Waals, hydrophobic, and hydrogen bond interactions for all the systems. Remarkably, OSM-9 and OCR-2 shifted towards aromatic and dispersion forces compared to TRPV1 at the expense of hydrogen bonds and polar and electrostatic interactions. Comparison between capsaicin binding modes and contacts between OCR-2 and OSM-9 suggests the former presents more relevant interactions with the ligand, aligning with previous experimental results that favor OCR-2 as the main target of capsaicin in C. elegans. Our findings highlight the structural diversity and overlap between TRPV and OSM-9/OCR-2 channels, and the importance of these channels in antinociceptive mechanisms, which could encourage applications for translational research with potential implications in human health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".