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Record W4390098967 · doi:10.1126/sciadv.adj0807

Energetic driving force for LHCII clustering in plant membranes

2023· article· en· W4390098967 on OpenAlexfundno aff
Premashis Manna, Madeline Hoffmann, Thomas W. Davies, Katherine H. Richardson, Matthew P. Johnson, Gabriela S. Schlau‐Cohen

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilCanadian Institute for Advanced ResearchNational Science Foundation
KeywordsMembraneWork (physics)Cluster analysisChemical physicsChemistryEnergy landscapeMoleculeBiophysicsPhysicsThermodynamicsBiologyBiochemistryComputer science

Abstract

fetched live from OpenAlex

Plants capture and convert solar energy in a complex network of membrane proteins. Under high light, the luminal pH drops and induces a reorganization of the protein network, particularly clustering of the major light-harvesting complex (LHCII). While the structures of the network have been resolved in exquisite detail, the thermodynamics that control the assembly and reorganization had not been determined, largely because the interaction energies of membrane proteins have been inaccessible. Here, we describe a method to quantify these energies and its application to LHCII. Using single-molecule measurements, LHCII proteoliposomes, and statistical thermodynamic modeling, we quantified the LHCII-LHCII interaction energy as ~−5 k B T at neutral pH and at least −7 k B T at acidic pH. These values revealed an enthalpic thermodynamic driving force behind LHCII clustering. Collectively, this work captures the interactions that drive the organization of membrane protein networks from the perspective of equilibrium statistical thermodynamics, which has a long and rich tradition in biology.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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 teacher head, 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

Citations8
Published2023
Admission routes1
Has abstractyes

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