Principal Component Analysis of Binding Energies for Single-Point Mutants of hT2R16 Bound to an Agonist Correlate with Experimental Mutant Cell Response
Bibliographic record
Abstract
Directed evolution is a technique that enables the identification of mutants of a particular protein that carry a desired property by successive rounds of random mutagenesis, screening, and selection. This technique has many applications, including the development of G protein-coupled receptor-based biosensors and designer drugs for personalized medicine. Although effective, directed evolution is not without challenges and can greatly benefit from the development of computational techniques to predict the functional outcome of single-point amino acid substitutions. In this article, we describe a molecular dynamics-based approach to predict the effects of single amino acid substitutions on agonist binding (salicin) to a human bitter taste receptor (hT2R16). An experimentally determined functional map of single-point amino acid substitutions was used to validate the whole-protein molecular dynamics-based predictive functions. Molecular docking was used to construct a wild-type agonist-receptor complex, providing a starting structure for single-point substitution simulations. The effects of each single amino acid substitution in the functional response of the receptor to its agonist were estimated using three binding energy schemes with increasing inclusion of solvation effects. We show that molecular docking combined with molecular mechanics simulations of single-point mutants of the agonist-receptor complex accurately predicts the functional outcome of single amino acid substitutions in a human bitter taste receptor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".