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WIREs Comput Mol Sci
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Electronic energy transfer in biomacromolecules

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Electronic energy transfer is widely used as a molecular ruler to interrogate the structure of biomacromolecules, and performs a key task in photosynthesis by transferring collected energy through specialized pigment–protein complexes. Förster theory, introduced over 70 years ago, allows linking transfer rates to simple structural and spectroscopic properties of the energy‐transferring molecules. In biosystems, however, significant deviations from Förster behavior often arise due to breakdown of the ideal dipole approximation, dielectric screening effects due to the biological environment, or departure from the weak‐coupling regime. In this review, we provide a concise overview of advances in simulations of energy transfer in biomacromolecules that allow overcoming the main limitations of Förster theory. We first discuss advances in quantum chemical methods to compute electronic couplings, their extension to multiscale formulations to include screening effects, and strategies to treat the interplay between coupling fluctuations and energy transfer dynamics. We then examine the spectral overlap term, and how this quantity can be estimated from simulations of the spectral density of exciton–phonon coupling. Finally, we discuss rate theories that can describe energy transfers in situations where strong coupling leads to delocalized excitions, a common situation found in closely packed multichromophoric systems such as photosynthetic complexes and nucleic acids. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Theoretical and Physical Chemistry > Spectroscopy
(a) Schematic energy‐level diagram of an energy transfer process. (b) Spectral overlap between donor emission and acceptor absorption
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Downhill exciton relaxation rates in a DA dimer, as predicted by the Förster equation (F, orange lines), modified Redfield theory (mR, red lines), and standard Redfield theory (sR, blue lines). Solid lines correspond to the model B777 SD, while dashed lines correspond to the calculated B800 spectral density (SD) including high‐frequency vibrations. The two SDs have total reorganization energies of ∼100 and ∼220 cm−1, respectively, and comparable reorganization energy for lower frequencies (500 cm−1). We used the same SD for donor and acceptor. In (a) and (b), we plot downhill rates as a function of the energy gap between D and A, for electronic coupling values of (a) V = 100 cm−1 or (b) V = 20 cm−1. (c) Downhill rates as a function of the electronic coupling V, for energy gap Δℰ=100 cm−1. (d) Downhill rates as a function of the total reorganization energy of the SD. Here, we use Δℰ=100 cm−1 and an intermediate coupling V = 40 cm−1. In all cases, the temperature was fixed at 300 K
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Spectral overlap between a carotenoid donor and a bacteriochlorophyll acceptor. The filled curves represent the densities of states fA and fD, whilst the dashed line represents the value of the spectral overlap J as a function of the vertical excitation energy of the acceptor, in logarithmic scale
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Markov chain sampling technique used to model electronic energy transfer (EET) observables
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Electronic coupling fluctuations computed for different biosystems. Left panels show the time series of the couplings and the resulting distribution. Right panels show the geometrical fluctuations of the donor–acceptor pair that give rise to coupling fluctuations. (a) Singlet EET (SEET) coupling between (R)‐flurbiprofen and Trp214 in human serum albumin (HSA), (b) triplet–triplet energy transfer (TEET) coupling between two adenines in a polyA–polyT DNA sequence, (c) SEET coupling between chlorophylls a603 and a609 in the CP29 photosynthetic complex, (d) TEET coupling between chlorophyll a603 and violaxanthin in the CP29 photosynthetic complex
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Mean percent error in absolute electronic coupling computed at different levels of theory for selected systems. (a) Singlet electronic energy transfer (EET) Coulomb couplings for napthalene, perylene, phycoerythrobilin (PEB) and BChl–BPheo pairs computed from quantum mechanical (QM) transiton densities (VCoul) and dipoles (Vdd) compared with SAC‐CI/6‐31G reference values. (b) Triplet EET couplings for ethylene, napthalene, thymine, and adenine pairs computed using the supermolecule approach compared to EOM‐CCSD/6‐31G reference values
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Distribution of dielectric screening factors estimated using the polarizable molecular mechanics (MMPol) model along a molecular dynamics (MD) simulation for different biosystems (left panels) and representation of chromophore transition densities and corresponding residue contributions (in cm−1) to the environment‐mediated coupling term (right panels). (a) (R)‐flurbiprofen and Trp214 in human serum albumin (HSA), (b) PEB50/61C and PEB50/61D bilin pigments in the PE545 complex, (c) interdimer and (d) intradimer α‐BChl–β‐BChl couplings in the B850 ring of the LH2 complex
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Theoretical and Physical Chemistry > Spectroscopy
Structure and Mechanism > Computational Biochemistry and Biophysics

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