Functional and topological diversity of LOV domain photoreceptors
Bibliographic record
Abstract
Light-oxygen-voltage sensitive (LOV) flavoproteins are ubiquitous photoreceptors that mediate responses to environmental cues. Photosensory inputs are transduced into signaling outputs via structural rearrangements in sensor domains that consequently modulate the activity of an effector domain or multidomain clusters. Establishing the diversity in effector function and sensor-effector topology will inform what signaling mechanisms govern light-responsive behaviors across multiple kingdoms of life and how these signals are transduced. Here, we report the bioinformatics identification of over 6,700 candidate LOV domains (including over 4,000 previously unidentified sequences from plants and protists), and insights from their annotations for ontological function and structural arrangements. Motif analysis identified the sensors from ∼42 million ORFs, with strong statistical separation from other flavoproteins and non-LOV members of the structurally related Per-aryl hydrocarbon receptor nuclear translocator (ARNT)-Sim family. Conserved-domain analysis determined putative light-regulated function and multidomain topologies. We found that for certain effectors, sensor-effector linker length is discretized based on both phylogeny and the preservation of α-helical heptad repeats within an extended coiled-coil linker structure. This finding suggests that preserving sensor-effector orientation is a key determinant of linker length, in addition to ancestry, in LOV signaling structure-function. We found a surprisingly high prevalence of effectors with functions previously thought to be rare among LOV proteins, such as regulators of G protein signaling, and discovered several previously unidentified effectors, such as lipases. This work highlights the value of applying genomic and transcriptomic technologies to diverse organisms to capture the structural and functional variation in photosensory proteins that are vastly important in adaptation, photobiology, and optogenetics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".