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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2409.10505 |
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| _version_ | 1866917777141399552 |
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| author | Singh, Davinder Chuang, Chern Brumer, Paul |
| author_facet | Singh, Davinder Chuang, Chern Brumer, Paul |
| contents | Designing a model of retinal isomerization in Rhodopsin, the first step in vision, that accounts for both experimental transient and stationary state observables is challenging. Here, multi-objective Bayesian optimization is employed to refine the parameters of a minimal two-state-two-mode (TM) model describing the photoisomerization of retinal in Rhodopsin. With an appropriate selection of objectives, the optimized retinal model predicts excitation wavelength-dependent fluorescence spectra that closely align with experimentally observed non-Kasha behavior in the non-equilibrium steady state. Further, adjustments to the potential energy surface within the TM model reduce the discrepancies across the time domain. Overall, agreement with experimental data is excellent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10505 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Optimization of non-Kasha Behavior and of Transient Dynamics in Model Retinal Isomerization Singh, Davinder Chuang, Chern Brumer, Paul Biological Physics Quantum Physics Designing a model of retinal isomerization in Rhodopsin, the first step in vision, that accounts for both experimental transient and stationary state observables is challenging. Here, multi-objective Bayesian optimization is employed to refine the parameters of a minimal two-state-two-mode (TM) model describing the photoisomerization of retinal in Rhodopsin. With an appropriate selection of objectives, the optimized retinal model predicts excitation wavelength-dependent fluorescence spectra that closely align with experimentally observed non-Kasha behavior in the non-equilibrium steady state. Further, adjustments to the potential energy surface within the TM model reduce the discrepancies across the time domain. Overall, agreement with experimental data is excellent. |
| title | Machine Learning Optimization of non-Kasha Behavior and of Transient Dynamics in Model Retinal Isomerization |
| topic | Biological Physics Quantum Physics |
| url | https://arxiv.org/abs/2409.10505 |