Monte Carlo Modeling of Spectral Diffusion Employing Multiwell Protein Energy Landscapes: Application to Pigment–Protein Complexes Involved in Photosynthesis
Bibliographic record
Abstract
We are reporting development and initial applications of the light-induced and thermally induced spectral diffusion modeling software, covering nonphotochemical spectral hole burning (NPHB), hole recovery, and single-molecule spectroscopy and involving random generation of the multiwell protein energy landscapes. The model includes tunneling and activated barrier-hopping in both ground and excited states of a protein-chromophore system. Evolution of such a system is predicted by solving the system of rate equations. Using the barrier parameters from the range typical for the energy landscapes of the pigment-protein complexes involved in photosynthesis, we (a) show that realistic cooling of the sample must result in proteins quite far from thermodynamic equilibrium, (b) demonstrate hole evolution in the cases of burning, fixed-temperature recovery and thermocycling that mostly agrees with the experiment and modeling based on the NPHB master equation, and (c) explore the effects of different protein energy landscapes on the antihole shape. Introducing the multiwell energy landscapes and starting the hole burning experiments in realistic nonequilibrium conditions are not sufficient to explain all experimental observations even qualitatively. Therefore, for instance, one is required to invoke the modified NPHB mechanism where a complex interplay of several small conformational changes is poising the energy landscape of the pigment-protein system for downhill tunneling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".