Spectral tuning in vertebrate short wavelength‐sensitive 1 (SWS1) visual pigments: Can wavelength sensitivity be inferred from sequence data?
Bibliographic record
Abstract
The molecular mechanisms underlying the enormous diversity of visual pigment wavelength sensitivities found in nature have been the focus of many molecular evolutionary studies, with particular attention to the short wavelength-sensitive 1 (SWS1) visual pigments that mediate vision in the ultraviolet to violet range of the electromagnetic spectrum. Over a decade of study has revealed that the remarkable extension of SWS1 absorption maxima (λ max ) into the ultraviolet occurs through a deprotonation of the Schiff base linkage of the retinal chromophore, a mechanism unique to this visual pigment type. In studies of visual ecology, there has been mounting interest in inferring visual sensitivity at short wavelengths, given the importance of UV signaling in courtship displays and other behaviors. Since experimentally determining spectral sensitivities can be both challenging and time-consuming, alternative strategies such as estimating λ max based on amino acids at sites known to affect spectral tuning are becoming increasingly common. However, these estimates should be made with knowledge of the limitations inherent in these approaches. Here, we provide an overview of the current literature on SWS1 site-directed mutagenesis spectral tuning studies, and discuss methodological caveats specific to the SWS1-type pigments. We focus particular attention on contrasting avian and mammalian SWS1 spectral tuning mechanisms, which are the best studied among vertebrates. We find that avian SWS1 visual pigment spectral tuning mechanisms are fairly consistent, and therefore more predictable in terms of wavelength absorption maxima, whereas mammalian pigments are not well suited to predictions of λ max from sequence data alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".