Light-Harvesting In Photosynthetic Proteins

 

These processes are quantum mechanical in nature, influenced by the smallest structural and environmental changes.

Why study light-harvesting?

  1. Gaining Mechanistic Insights:
    When two or more pigments come close together, their properties deviate from their individual ones. Molecular excitons are formed when an excitation is delocalized across multiple pigments. In the LHC, sunlight leads to collective excitations, which can be stored and transferred more efficiently than individual excitations. The proteins do not only orient the pigments in a specific way, but also fine-tune their individual electronic properties to optimize the collective properties of the LHC. Elucidating the individual effects of proteins, pigments and cofactors helps to understand and interpret experiments.
  2. Tailoring Light-Harvesting Systems:
    Understanding the light harvesting mechanism opens the door to genetically engineered plants that can survive under extreme high or low light conditions.

What are we studying?

The LHCs we are currently investigating include:

 

 

 

 

 

 

 

 

 

 

  • LH2 from Rhodospirillum (Rs.) molischianum:
    LH2 is a peripheral antenna complex found in purple bacteria that perform anoxygenic photosynthesis. The LH2 complex contains specialized pigments that absorb light at longer wavelengths than pigments in plants, allowing purple bacteria to grow under trees.
  • CP29 from higher plants:
    CP29 is an antenna complex associated with photosystem II (PSII) in higher plants. This complex balances two functions: absorbing light and transferring it to the core of PSII, and participating in photoprotection mechanisms to prevent damage from excess light.
  • LHCII from higher plants:
    LHCII is the most abundant antenna complex in higher plants and serves as the primary LHC of PSII. It is responsible for absorbing sunlight and transferring the energy to the reaction center of PSII coordination with CP29.

How are we studying it?

The simulation of energy transfer in photosynthesis at the atomic level is a massive computational task.

  • The combination of quantum mechanics and molecular mechanics (QM/MM, Nobel Prize 2013) is a well-established method

Photosynthetic pigments are large (>100 atoms)

  • Machine Learning (ML) can replace the expensive QM calculations
  • ML/MM  simulations enable accurate simulation of large systems

Current topics of interest

  • ML/MM dynamics
    • Describing the movements of pigments with force fields is not accurate enough to capture certain fluctuations in their excited state properties. Therefore, we try to achieve QM-like accuracy by training ML models on DFTB forces.
  • Carotenoids
    • The past decades of LHC research have focused on chlorophylls to model energy transfer. However, there are other types of pigments to consider, such as carotenoids (see below). They are notoriously difficult to model using QM methods, so they are generally ignored in many studies. This is accurate enough in many scenarios, but not always. For example, under excessive light, plants need to protect themselves from photodamage. The underlying mechanisms are likely to involve carotenoids.

  • Charge Transfer States
    • There are different types of excitations to consider when modeling excitation energy transfer. The most common type is an excitation localized on a single chromophore. However, there are also excitations associated with intermolecular charge transfer that have gained attention in recent years.

Potential projects for students

  • Exciton Transfer in CP43
     
    • PS II Super-complex:

 

 

Selected publications

Phys. Chem. Chem. Phys., 2020,22, 10500-10518
https://doi.org/10.1039/C9CP05753F

Phys. Chem. Chem. Phys., 2024,26, 19469-19496
https://doi.org/10.1039/D4CP02116A

ChemRxiv, 2024
https://doi.org/10.26434/chemrxiv-2024-llc9d