Structural Modeling of Bitter Taste Receptors Employing A Multi-Template Homology Approach for Ligand Development: The TAS2R14 Case Study Validated Against CryoEM Structures
Bibliographic record
Abstract
Bitter taste receptors (TAS2Rs), comprising a subfamily of G protein-coupled receptors (GPCRs), mediate bitter taste perception and are commonly proposed to have evolved to detect the ingestion of potentially harmful compounds. TAS2R14, although the best structurally characterized member of the class, remains poorly defined due to the flexible nature of extracellular loops, particularly extracellular loop 2 (ECL2). We present a TAS2R14 homology model (R14HM) with particular focus on resolving topologically challenging regions such as ECL2. Validation through membrane-embedded molecular dynamics (MD) simulations, MolProbity analysis, and stereochemical assessments confirmed its structural accuracy and stability. Recently released CryoEM structures of TAS2R14 enabled comparisons, corroborating the predictive accuracy of the model and the methods used to generate it. To investigate ligand interactions, a curated library of bitter compounds, including phytochemicals, endocrine ligands, and humulone analogs, was docked into the R14HM, followed by MD refinement and MM-GBSA binding energy calculations. Finally, both the R14HM and the available Cryo-EM structures are compared to the AlphaFold-generated model, and the limitations and inaccuracies in the AlphaFold model are discussed. This work establishes a validated framework for TAS2R14 structure-based ligand discovery and highlights key considerations for modeling low-homology Class A GPCRs, bridging computational predictions and experimental findings and re-emphasizes the need for caution when using structures provided by pattern recognition software.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".