The function of light-harvesting complexes is determined by a complex network of dynamic interactions among all the different components: the aggregate of pigments, the protein, and the surrounding environment. Complete and reliable predictions on this type of composite systems can be only achieved with an atomistic description. In the last decades there have been important advances in the atomistic modeling of light-harvesting complexes. These advances have involved both the completeness of the physical models and the accuracy and effectiveness of the computational protocols. In this perspective, we present an overview of the main theoretical and computational breakthroughs attained so far in the field, with particular focus on the important role played by the protein and its dynamics. We then discuss the open problems in their accurate modeling that still need to be addressed. To illustrate an effective computational workflow for the modeling of LH complexes, we take as an example the plant antenna complex CP29 and its H111N mutant.

Antenna complexes in photosystems of plants and green algae are able to switch between a light-harvesting unquenched conformation and a quenched conformation so to avoid photodamage. When the switch is activated, nonphotochemical quenching (NPQ) mechanisms take place for an efficient deactivation of excess excitation energy. The molecular details of these mechanisms have not been fully clarified but different hypotheses have been proposed. Among them, a popular one involves excitation energy transfer (EET) from the singlet excited Chls to the lowest singlet state (S 1) of carotenoids. In this work, we combine such model with µs-long molecular dynamics simulations of the CP29 minor antenna complex to investigate how conformational fluctuations affect the electronic couplings and the final EET quenching. The computational framework is applied to both CP29 embedding violaxanthin and zeaxantin in its L2 site. Our results demonstrate that the EET model is rather insensitive to physically reasonable variations in single chlorophyll-carotenoid couplings, and that very large conformational changes would be needed to see the large variation of the complex lifetime expected in the switch from light-harvesting to quenched state. We show, however, that a major role in regulating the EET quenching is played by the S 1 energy of the carotenoid, in line with very recent spectroscopy experiments.

Light-harvesting complexes of plants exert a dual function of light-harvesting (LH) and photoprotection through processes collectively called nonphotochemical quenching (NPQ). While LH processes are relatively well characterized, those involved in NPQ are less understood. Here, we characterize the quenching mechanisms of CP29, a minor LHC of plants, through the integration of two complementary enhanced-sampling techniques, dimensionality reduction schemes, electronic calculations and the analysis of cryo-EM data in the light of the predicted conformational ensemble. Our study reveals that the switch between LH and quenching state is more complex than previously thought. Several conformations of the lumenal side of the protein occur and differently affect the pigments’ relative geometries and interactions. Moreover, we show that a quenching mechanism localized on a single chlorophyll-carotenoid pair is not sufficient but many chlorophylls are simultaneously involved. In such a diffuse mechanism, short-range interactions between each carotenoid and different chlorophylls combined with a protein-mediated tuning of the carotenoid excitation energies have to be considered in addition to the commonly suggested Coulomb interactions.

We propose a machine learning (ML)-based strategy for an inexpensive calculation of excitonic properties of lightharvesting complexes (LHCs). The strategy uses classical molecular dynamics simulations of LHCs in their natural environment in combination with ML prediction of the excitonic Hamiltonian of the embedded aggregate of pigments. The proposed ML model can reproduce the effects of geometrical fluctuations together with those due to electrostatic and polarization interactions between the pigments and the protein. The training is performed on the chlorophylls of the major LHC of plants, but we demonstrate that the model is able to extrapolate well beyond the initial training set. Moreover, the accuracy in predicting the effects of the environment is tested on the simulation of the small changes observed in the absorption spectra of the wild-type and a mutant of a minor LHC.

Electronic couplings are key to understanding exciton delocalization and transport in natural and artificial light harvesting processes. We develop a method to compute couplings in multichromophoric aggregates embedded in complex environments without running expensive quantum chemical calculations. We use a transition charge approximation to represent the quantum mechanical transition densities of the chromophores and an atomistic and polarizable classical model to describe the environment atoms. We extend our framework to estimate transition charges directly from the chromophore geometry, i.e., bypassing completely the quantum mechanical calculations using a regression approach. The method allows to rapidly compute accurate couplings for a large number of geometries along molecular dynamics trajectories.