Energetic driving force for LHCII clustering in plant membranes
Bibliographic record
Abstract
ABSTRACT Plants protect themselves against photodamage from excess energy using a process known as non-photochemical quenching (NPQ). A significant fraction of NPQ is induced by a ΔpH across the membrane, which changes the conformation, composition, and organization of the antenna complexes. In particular, clustering of the major light-harvesting complex (LHCII) has been observed, yet the thermodynamic driving force behind this reorganization has not been determined, largely because measurements of membrane protein interaction energies have not been possible. Here, we introduce a method to quantify membrane protein interaction energies and its application to the thermodynamics of LHCII clusters. By combining single-molecule measurements of LHCII-proteoliposomes at different protein densities and a rigorous analysis of LHCII clusters and photophysics, we quantified the LHCII-LHCII interaction energy to be approximately -5 k B T at neutral pH and at least -7 k B T at acidic pH. From these values, we found the thermodynamic driving force for LHCII clustering was dominated by these enthalpic contributions. Collectively, this work captures the membrane protein-protein interactions responsible for LHCII clustering from the perspective of equilibrium statistical thermodynamics, which has a long and rich tradition in biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".