Experimental lipophilicity scale for coded and noncoded amino acid residues
Bibliographic record
Abstract
Among other features, the polarity of amino acid residues is the key parameter for understanding their role in proteins. The wide occurrence of protein modifications in nature and the advent of genetic code engineering techniques created a need for an experimental polarity value integrating both coded (canonical) and noncoded (noncanonical) residues on one universal scale. To address this issue, this work reports on a polarity scale based on the experimental lipophilicity of methyl esters of N-acetylamino acids. The derivatization of amino acids was performed in two steps under mild conditions that allowed conversion of a wide array of amino acids into analytical derivatives. The partitioning/distribution between octan-1-ol and water/buffer was measured using the intensity of the NMR signal as a characteristic for the concentration. The reference set of twenty coded amino acids generated log P values spanning 5.1 units: from tryptophan being the most hydrophobic to aspartate being the most hydrophilic. Furthermore, lipophilicity was measured for a set of analogues of phenylalanine, tyrosine, tryptophan, methionine, proline, and lysine that are typical in nature and/or laboratory practice. The polarity scale reported here will aid the rationalization of amino acid replacements in proteins, and will guide further efforts in experimental genetic code engineering.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".