Photosynthetic Light-Harvesting Is Tuned by the Heterogeneous Polarizable Environment of the Protein
Bibliographic record
Abstract
In photosynthesis, special antenna proteins that contain multiple light-absorbing molecules (chromophores) are able to capture sunlight and transfer the excitation energy to reaction centers with almost 100% quantum efficiencies. The critical role of the protein scaffold in holding the appropriate arrangement of the chromophores is well established and can be intuitively understood given the need to keep optimal dipole-dipole interactions between the energy-transferring chromophores, as described by Förster theory more than 60 years ago. However, the question whether the protein structure can also play an active role by tuning such dipole-dipole interactions has not been answered so far, its effect being rather crudely described by simple screening factors related to the refractive index properties of the system. Here, we present a combined quantum chemical/molecular mechanical approach to compute electronic couplings that accounts for the heterogeneous dielectric nature of the protein-solvent environment in atomic detail. We apply the method to study the effect of dielectric heterogeneity in the energy migration properties of the PE545 principal light-harvesting antenna of the cryptomonad Rhodomonas CS24. We find that dielectric heterogeneity can profoundly tune by a factor up to ∼4 the energy migration rates between chromophore sites compared to the average continuum dielectric view that has historically been assumed. Our results indicate that engineering of the local dielectric environment can potentially be used to optimize artificial light-harvesting antenna systems.
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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 | 0.000 | 0.000 |
| 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".