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Record W2292491794 · doi:10.1073/pnas.1509428113

Functional and topological diversity of LOV domain photoreceptors

2016· article· en· W2292491794 on OpenAlexafffund
Spencer T. Glantz, Eric Carpenter, Michael Melkonian, Kevin H. Gardner, Edward S. Boyden, Gane Ka‐Shu Wong, Brian Y. Chow

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeNIH Office of the DirectorAdvanced Research Projects AgencyNational Institutes of HealthAlberta InnovatesNational Institute of Mental HealthDivision of Emerging Frontiers in Research and InnovationBrain Research FoundationOffice of the Secretary of DefenseHuman Frontier Science ProgramAlberta Innovates - Technology FuturesBroad InstituteNational Institute on Drug AbuseUniversity of PennsylvaniaCancer Prevention and Research Institute of TexasNew York Stem Cell FoundationDefense Advanced Research Projects AgencyMinistry of Innovation and Advanced EducationW. W. Smith Charitable TrustInstitution of Engineering and TechnologyBeijing Institute of Genomics, Chinese Academy of SciencesDivision of Chemical, Bioengineering, Environmental, and Transport SystemsDirectorate for EngineeringSkolkovo Institute of Science and TechnologyUniversity of Pennsylvania Health SystemNational Institute of General Medical SciencesWallace H. Coulter FoundationAlfred P. Sloan FoundationNational Science Foundation
KeywordsDiversity (politics)Topology (electrical circuits)Domain (mathematical analysis)MathematicsSociologyCombinatorics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.238
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations165
Published2016
Admission routes2
Has abstractyes

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