The role of the local environment on the structural heterogeneity of carotenoid β-ionone rings

Pishchalnikov, Roman Y.; Yaroshevich, Igor A.; Zlenko, Dmitry V.; Tsoraev, Georgy V.; Осипов, Е.М.; Lazarenko, Vladimir A.; Parshina, E. Yu.; Chesalin, Denis D; Sluchanko, Nikolai N.; Maksimov, Eugene G.

doi:10.1007/s11120-022-00955-2

Cited by 12 publications

(14 citation statements)

References 49 publications

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“…decreased quite fast in the first ten generations, and then there was a slow asymptotic stagnation. Similar behavior of was observed in the modeling of monomeric pigments' absorption in solvents [41,42]. The maximum number of generations was set to 100.…”

Section: Discussionmentioning

confidence: 84%

“…DE seems to be a promising solution to fit experimental data of PPC. Studies of the optical properties of monomeric photosynthetic pigments in different solvents with DE have proved to be quite optimistic [41,42]. To proceed with PSIIRC modeling considering MCM for inhomogeneous broadening, the effect of MC on the averaged spectra has to be investigated.…”

Section: Discussionmentioning

confidence: 99%

“…First of all, it should be stressed that to achieve better fitting, at least another three free parameters must be added to the current set of eleven. These are the frequency, the Huang-Rhys factor and the damping factor, { , , } of the lowest frequency mode used to calculate the spectral density of a pigment [40][41][42]. At the moment, there are some technical problems preventing such improvement of our software and considering these three parameters in the fitting, but this is our immediate goal to enhance computational procedures.…”

Section: Discussionmentioning

confidence: 99%

“…To deal with this situation, we have developed software combining the DE algorithm and procedures for the calculation of the spectra of photosynthetic pigments and pigment-protein complexes [40,41]. Starting with the modeling of linear absorption of monomeric Chl, bacteriochlorophyll and carotenoids in different solvents, the best DE strategy and its optimal settings have been found [41,42]. The next step was to explore the prospect of using DE to fit the linear spectroscopy of a small PPC, such as PSIIRC.…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo Simulation Affects Convergence of Differential Evolution: A Case of Optical Response Modeling

et al. 2022

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It is known that the protein surrounding, as well as solvent molecules, has a significant influence on optical spectra of organic pigments by modulating the transition energies of their electronic states. These effects manifest themselves by a broadening of the spectral lines. Most semiclassical theories assume that the resulting lineshape of an electronic transition is a combination of homogeneous and inhomogeneous broadening contributions. In the case of the systems of interacting pigments such as photosynthetic pigment–protein complexes, the inhomogeneous broadening can be incorporated in addition to the homogeneous part by applying the Monte Carlo method (MCM), which implements the averaging over static disorder of the transition energies. In this study, taking the reaction center of photosystem II (PSIIRC) as an example of a quantum optical system, we showed that differential evolution (DE), a heuristic optimization algorithm, used to fit the experimentally measured data, produces results that are sensitive to the settings of MCM. Applying the exciton theory to simulate the PSIIRC linear optical response, the number of minimum required MCM realizations for the efficient performance of DE was estimated. Finally, the real linear spectroscopy data of PSIIRC were fitted using DE considering the necessary modifications to the implementation of the optical response modeling procedures.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monte Carlo Simulation Affects Convergence of Differential Evolution: A Case of Optical Response Modeling

et al. 2022

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“…Notably, ring puckering was recognized to be a determinant of heterogeneity in OCP. 45,55 Here we assign the structures with f # 0°to the p − conformation (52% of the total number of trajectories), and all the other structures (with f > 0°at t = t 0 ) to the p + conformation (Fig. S14 in the ESI †).…”

Section: The S 1 / S 0 Relaxation Processmentioning

confidence: 99%

How orange carotenoid protein controls the excited state dynamics of canthaxanthin

Arcidiacono,

Accomasso,

Cupellini

et al. 2023

Chem. Sci.

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A Novel Approach for Solubility and Bioavailability Enhancement of Canthaxanthin Obtained from Dietzia natronolimnaea HS-1 by Canthaxanthin-V-Amylose Complex

Bahrololoumi,

Khosroshahi,

Razavi

et al. 2024

Food Bioprocess Technol

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The role of the local environment on the structural heterogeneity of carotenoid β-ionone rings

Cited by 12 publications

References 49 publications

Monte Carlo Simulation Affects Convergence of Differential Evolution: A Case of Optical Response Modeling

Monte Carlo Simulation Affects Convergence of Differential Evolution: A Case of Optical Response Modeling

How orange carotenoid protein controls the excited state dynamics of canthaxanthin

A Novel Approach for Solubility and Bioavailability Enhancement of Canthaxanthin Obtained from Dietzia natronolimnaea HS-1 by Canthaxanthin-V-Amylose Complex

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